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How to use sorted() and sort() in Python

Whenever you visit a pharmacy and ask for a particular medicine, have you noticed something? It hardly takes any time for the pharmacist to find it among several medicines. This is because all the items are arranged in a certain fashion which helps them know the exact place to look for. They may be arranged in alphabetical order or according to their category such as ophthalmics or neuro or gastroenterology and so on. A proper arrangement not only saves time but also make operations simple and easy, hence sorting is essential.At some point or the other, every programmer needs to learn one of the most essential skills,  Sorting. Python sorting functions comprise of a lot of features to perform basic sorting or customize ordering according to the programmer’s needs.Basically, sorting is a technique that is used to arrange data in an increasing and decreasing fashion according to some linear relationship among the data elements. You can sort numbers, or names or records of any kind in any fashion according to your needs. Sorting techniques can be used to arrange a list of mail recipients in an alphabetical manner.There are a number of sorting algorithms in Python starting from Bubble Sort, Insertion Sort, Quick Sort, Merge Sort, Selection Sort and so on. In this article we will look into how to use sorted() and sort() in Python. To learn more about other concepts of Python, go through our Python Tutorials.What is the need for Sorting?In simple terms, sorting means arranging data systematically. If the data you want to work with  is not sorted you will face problems in finding your desired element.The main advantages of sorting elements in Python are:When you work with sorting modules, you will get to know about a large number of language components.Sorting Algorithms provide an abstract way of learning about the accuracy of your program without worrying about system developments or dependencies.It will help you in understanding the program complexity and speed and how to increase the efficiency.How to order values using sorted()?sorted() is a built-in function that accepts an iterable and returns the sorted values in ascending order by default which contains the iterable items.Sorting Numbers using sorted()Let us define a list of integers called num_list and pass it as an argument to sorted():>>> num_list = [4, 10, 1, 7] >>> sorted(num_list) [1, 4, 7, 10] >>> num_list [4, 10, 1, 7]Some of the insights we gain from the code above are:sorted() is a built-in function found in the Python Standard Library. It cannot be defined.sorted() orders the values in num_list in ascending order by default, i.e. smallest to largest.The original values of num_list are not changed.sorted() being called, returns an ordered list of values.Since sorted() function returns the list in order, we can assign the returned list to another variable:>>> num_list = [4, 10, 1, 7] >>> sorted_list = sorted(num_list) >>> sorted_list [1, 4, 7, 10] >>> num_list [4, 10, 1, 7]A new variable sorted_list is created which holds the output of sorted().You can also use sorted() to sort tuples and sets just like numbers:>>> tuples = (4, 10, 1, 7) >>> sets = {10, 5, 10, 0, 2} >>> sorted_tuples = sorted(numbers_tuple) >>> sorted_sets = sorted(numbers_set) >>> sorted_tuples [1, 4, 7, 10] >>> sorted_sets [0, 2, 5, 10]The definition of sorted() states that it will return a new list whatever the input may be. So even if the input variables are tuples and sets, sorted() always returns a list.You can also perform type casting in cases where you need to match the returned object with the input type:>>> tuples = (4, 10, 1, 7) >>> sets = {10, 5, 10, 0, 2} >>> sorted_tuples = sorted(numbers_tuple) >>> sorted_sets = sorted(numbers_set) >>> sorted_tuples [1, 4, 7, 10] >>> sorted_sets [0, 2, 5, 10] >>> tuples(sorted_tuples) (1, 4, 7, 10) >>> sets(sorted_sets) {0, 2, 5, 10}In the code above, you can see the sorted_tuples when cast to tuples is retained in an ordered manner whereas sorted_sets when casted does not return an order list since it is unordered by definition.Sorting Strings using sorted()Sorting of strings is just like sorting tuples and sets. sorted() iterates across each character of the input and returns a string order.An example of sorting str type using sorted():>>> num_string = '98765' >>> char_string = 'sorting is fun' >>> sorted_num_string = sorted(num_string) >>> sorted_char_string = sorted(char_string) >>> sorted_num_string ['5', '6', '7', '8', '9'] >>> sorted_char_string ['', '','f', 'g', 'i', 'i', 'n', 'n', 'o', 'r', 's', 's', 't','u']The str is treated as a list and sorted() iterates through each character including spaces.You can use .split() to change the behavior and clean the output and .join() to rejoin them together:>>> string = 'sorting is fun' >>> sorted_string = sorted(string.split()) >>> sorted_string ['fun', 'is', 'sorting'] >>> ' '.join(sorted_string) 'fun is sorting'The actual string is converted into a list of words using .split() and then it is sorted with sorted() and then again joined together using .join().How to use sorted() with a reverse Argument?The syntax of the sorted() function is sorted(iterable, /, *, key=None, reverse=False).The built-in function sorted() comprises of three parameters:iterable — Required. A sequence such as string, tuple or list and collection such as set or dictionary.key — Optional. A function that serves as a key or to customize the sort order. The argument is set to None by default.reverse — Optional. A boolean flag that reverses the order of sorting. If True, the sorted list is reversed. The default argument is False.reverse is an optional keyword argument that changes the sorting order according to the Boolean value assigned to it. The default value is False, which performs sorting in ascending order. However, if the value is given as True, descending sort occurs:>>> name_list = ['Markian', 'Alex', 'Suzzane', 'Harleen'] >>> sorted(name_list) ['Alex', 'Harleen', 'Markian', 'Suzzane'] >>> sorted(name_list, reverse=True) ['Suzzane', 'Markian', 'Harleen', 'Alex']In the example above, the sorting is done on the basis of the first alphabet. However, when sorted() encounters the reverse keyword with a True argument, the output is reversed.Another example to understand the behavior of the reverse keyword:>>> case_sensitive_names = ['Markian', 'alex', 'Suzzane', 'harleen'] >>> sorted(case_sensitive_names, reverse=True) ['harleen', 'alex', 'Suzzane', 'Markian'] >>> values_to_sort = [False, 1, 'A' == 'B', 1 <= 0] >>> sorted(values_to_sort, reverse=True) [1, False, False, False] >>> num_list = [7, 10, 0, 4] >>> sorted(num_list, reverse=False) [0, 4, 7, 10]How to use sorted() with a key Argument?The keyword argument key accepts a function and this function determines the resulting order by implementing itself in each value of the list.An example to illustrate sorting of a list using the function len(), which returns the length of the string, and providing the key argument as len:>>> word = 'pencil' >>> len(word) 6 >>> word_list = ['cherry', 'donut', 'Michigan', 'transcipt'] >>> sorted(word_list, key=len) ['donut', 'cherry', 'Michigan', 'transcript']The len() function determines the length of each item in the list and returns the list in ascending order (shortest to longest).Let us sort the earlier example using key where the first alphabet with different case was considered for the order:>>> case_sensitive_names = ['Markian', 'alex', 'Suzzane', 'harleen'] >>> sorted(case_sensitive_names, reverse=True) ['Markian', 'Suzzane', 'alex', 'harleen'] >>> sorted(case_sensitive_names, key=str.lower) ['alex', 'harleen', 'Markian', 'Suzzane']The key cannot make any changes to the original values in the list. So the final output will be the original sorted elements.Though key is considered as one of the most powerful components of sorted(), it has a number of limitations.The first limitation is that key accepts only single argument functions.An example of a function addition that accepts two arguments:>>> def addition(a, b):       return a + b >>> number_to_add = [1, 3, 5] >>> sorted(number_to_add , key=addition) Traceback (most recent call last):   File "stdin", line 5, in <module>     sorted(number_to_add, key=addition) TypeError: addition() missing 1 required positional argument: 'b'The program fails because whenever addition() is called during sorting, it receives only one element from the list at a time. The second argument is always missing.The second limitation is that the key function that is used must be able to handle all types of iterable values.An example to illustrate the second limitation:>>> cast_values = ['4', '5', '6', 'seven'] >>> sorted(cast_values, key=int) Traceback (most recent call last):   File "<stdin>", line 1, in <module> ValueError: invalid literal for int() with base 10: 'seven'The example above contains a list of numbers to be used by sorted() as strings. The key will try to convert the numbers to int. Each of the numbers represented as strings can be converted to int, but four cannot be. So a ValueError gets raised since four is not valid to cast into an int.Let us see an example to arrange an iterable by the last letter of each string:>>> def reverse(word):       return word[::-1] >>> words = ['cherry', 'cake', 'Michigan', 'transcript'] >>> sorted(words, key=reverse) ['cake', 'Michigan', 'transcript', 'cherry']The function reverse is defined to reverse the input string and then the function is used as the key argument. The slice syntax word[::-1] reverses the string and then the function reverse() takes all the elements one at a time and sorts the list according to the last alphabet.You can also use lambda function in the key argument instead of defining a regular function. A lambda is an anonymous function that does not have a name and executes just like normal functions. Lambda functions do not contain any statements.An example to show the previous code using a lambda function:>>> words = ['cherry', 'cake', 'Michigan', 'transcript'] >>> sorted(words, key = lambda x: x[::-1]) ['cake', 'Michigan', 'transcript', 'cherry']Here, the key is defined with lambda with no name and x is the argument. The slice syntax word[::-1] reverses each of the element and the reversed output is then used for sorting.An example to use key along with reverse argument:>>> words = ['cherry', 'cake', 'Michigan', 'transcript'] >>> sorted(words, key = lambda x: x[::-1], reverse = True) ['cherry', 'transcript', 'Michigan', 'cake']In this example, the order is reversed into a descending manner.Lambda functions can also be used to sort class objects according to their properties.An example to sort a group of students based on their grade in descending order:>>> from collections import namedtuple >>> Student = namedtuple('Student', 'name grade') >>> alex = Student('Alex', 95) >>> bob = Student('Bob', 87) >>> charlie = Student('Charlie', 91) >>> students = [alex, bob, charlie] >>> sorted(students, key=lambda x: getattr(x, 'grade'), reverse=True) [Student(name='Alex', grade=95), Student(name='Charlie', grade=91), Student(name='Bob', grade=87)]The namedtuple is used to produce classes with name and grade attributes. The lambda is used to get the grade property of each student and reverse is used to reverse the output into descending order so that the highest grades are arranged first.There are a lot of possible techniques to arrange elements using sorted() with key and reverse arguments. Lambda functions can also be helpful during sorting by making your code simple and clean.You can also use operator module functions like itemgetter() and attrgetter() to make your sorting program simpler and faster. The operator module is used to export a set of accessor functions in correspondence to the operators of Python.An example to illustrate operator module functions using key:>>> tuples = [      ('alex', 'B', 13),      ('bob', 'A', 12),      ('charles', 'B', 10),      ]>>> from operator import itemgetter>>> sorted(tuples, key=itemgetter(2))>>>[('charles', 'B', 10), ('bob', 'B', 12), ('alex', 'A', 13)]tuples is declared with the name, grade and age of three persons. The function itemgetter is imported from the module operator and then it is sorted by age and the output displayed in ascending order.How to order values using sort()?The .sort() which is quite similar to sorted() in naming has few differences than sorted(). The help documentation of Python will clear out the two critical differences between .sort() and sorted():>>> help(sorted) Help on built-in function sorted in module builtins: sorted(iterable, /, *, key=None, reverse=False)     Return a new list containing all items from the iterable in ascending order.     A custom key function can be supplied to customize the sort order, and the     reverse flag can be set to request the result in descending order. >>> help(list.sort) Help on method_descriptor: sort(self, /, *, key=None, reverse=False)     Stable sort *IN PLACE*.Firstly, .sort() is not a built-in function unlike sorted(). It is a method of list class and works only with lists. You cannot pass iterables to .sort().Secondly, .sort()  returns None and changes the values.Let us see the differences of code for .sort() and what impact it has on the code:>>> sort_numbers = [10, 2, 7, 3] >>> sort(sort_numbers) Traceback (most recent call last):   File "<stdin>", line 1, in <module> NameError: name 'sort' is not defined >>> sort_tuples = (10, 2, 7, 3) >>> sort_tuple.sort()>>> sort_tuples = (10, 2, 7, 3) >>> sort_tuple.sort() Traceback (most recent call last):   File "<stdin>", line 1, in <module> AttributeError: 'tuple' object has no attribute 'sort' >>> sorted_values = sort_numbers.sort() >>> print(sorted_values) None >>> sorted_values = sort_numbers.sort() >>> print(sorted_values)int(sort_numbers) [1, 2, 5, 6]The code above highlights some operational differences between .sort() and sorted():When any assignment is done to a new variable, it returns a None type. This is because .sort() function has no ordered output. The original order of sort_numbers is not maintained and is changed in place..sort() also contains the key and reverse optional keyword arguments just like sorted() which produces the same functionality.An example of .sort() using lambda to sort a list of phrases by the first letter of the third word:>>> sort_phrases = ['welcome to python',       'python is fun',       'python is easy'       ] >>> sort_phrases.sort(key=lambda x: x.split()[2][1], reverse=False) >>> sort_phrases ['python is easy', 'python is fun', 'welcome to python']Here, lambda is used to split each phrase into a list of words and then find the second letter of the third element for each phrase.Disadvantages of  Python SortingPython has some limitations when you try to sort values besides integers.Non-Comparable Data TypesYou cannot use sort data types that are different from each other. Python raises an error when sorted() is used on non-comparable data.An example to illustrate sorting of values of different data types:>>> mixed_values = [None, 5] >>> sorted(mixed_values) Traceback (most recent call last):   File "<stdin>", line 1, in <module> TypeError: '<' not supported between instances of 'int' and 'NoneType'Python raises a TypeError because it cannot sort None and int in the same list because of their incompatibility. It uses the less than operator ( < ) to determine the lower value in the order of the sort.If you try to compare the same values manually without using sorted(), it will still raise a TypeError because of non-comparable data types:>> None < 5 Traceback (most recent call last):   File "<stdin>", line 1, in <module> TypeError: '<' not supported between instances of 'NoneType' and 'int'However, if your list contains a combination of integers and strings that are all numbers, Python will cast them to comparable types using a list comprehension:>>> num_mix = [10, "5", 200, "11"] >>> sorted(num_mix) Traceback (most recent call last):   File "<stdin>", line 1, in <module> TypeError: '<' not supported between instances of 'str' and 'int' >>> # List comprehension to cast all values to integers >>> [int(z) for z in num_mix] [10, 5, 200, 11] >>> sorted([int(z) for z in num_mix]) [5, 10, 11, 200]int() converts all the string values in num_mix to integers and then sorted() compares all values and returns a sorted output.An example of a Python code of implicitly converting a value to another type:>>> values = [1, False, 0, 'a' == 'b', 0 >= 1] >>> sorted(values) [False, 0, False, False, 1]In the example above, all the elements in the list are converted to boolean type. 0 >= 1 evaluates to a False output. The number 1 and 0 are converted to True and False as bool type respectively.This particular example highlights an important characteristic of sorting– sort stability. Sorting ability means that sorting algorithms are always stable. The original order is retained even if multiple records have the same key argument.An example to illustrate sort stability:>>> values = [False, 0, 0, 3 == 4, 1, False, False] >>> sorted(values) [False, 0, 0, False, 0, False, 1]If you take a look at the original order and the sorted output, you’ll find that the expression 3 == 4 is casted to False and all sorted output is in the actual order. You can also perform complex sorts with the help of the knowledge of sort stability.Case-Sensitive SortingYou can use sorted() to sort a list of strings in ascending order which is alphabetical by default:>>> name_list = ['Markian', 'Alex', 'Suzzane', 'Harleen'] >>> sorted(name_list) ['Alex', 'Harleen', 'Markian', 'Suzzane']However, Python uses Unicode Code Point of the first letter of each string to evaluate the ascending order of the sort. If there are two names Al and al, Python will treat both of them differently.An example to return the Unicode Code Point of the first alphabet of each string:>>> case_sensitive_names = ['Markian', 'alex', 'Suzzane', 'harleen'] >>> sorted(case_sensitive_names) ['Markian', 'Suzzane', 'alex', 'harleen'] >>> # List comprehension for Unicode Code Point of first letter in each word >>> [(ord(name[0]), name[0]) for name in sorted(case_sensitive_names)] [(77, 'M'), (83, 'S'), (97, 'a'), (104, 'h')]In the example above, name[0] returns the first letter of the string and ord(name[0]) returns the Unicode Code Point. You can notice that even a comes before M alphabetically, the output has M before a. This is because the code point of M comes before a.Consider a situation where the first letter is the same for all the strings that need to be sorted. In such cases, the sorted() function will use the second letter to determine the order and if the second letter is also same, it will consider the third letter and so on, till the end of string:>>> similar_strings = ['zzzzzn', 'zzzzzc', 'zzzzza','zzzzze'] >>> sorted(similar_strings) ['zzzzza', 'zzzzzc', 'zzzzze', 'zzzzzn']Here, sorted() will compare the strings based on the sixth character since the first five characters are the same ( z ). The output will also depend on the last character of each string.An example of sorting elements having identical values:>>> different_lengths = ['zzzzz', 'zz', 'zzzz','z'] >>> sorted(different_lengths) ['z', 'zz', 'zzzz', 'zzzzz']In this case, the sorting order will be from the shortest to the longest. The shortest string z is ordered first and the longest string zzzzz is ordered at the last.When should you use .sort() and sorted()?Let us consider a case where you need to collect data from a race of 5k runners, the Python 5k Annual and then sort them. You will have to collect the runner’s bib number and the time it took to finish the race:>>> from collections import namedtuple >>> Runner_data = namedtuple('Runner', 'bibnumber duration')Each of the Runner_data will be added to a list called runners:>>> runners = [] >>> runners.append(Runner_data('2548597', 1200)) >>> runners.append(Runner_data('8577724', 1720)) >>> runners.append(Runner_data('2666234', 1600)) >>> runners.append(Runner_data('2425114', 1450)) >>> runners.append(Runner_data('2235232', 1620))     ...     ... >>> runners.append(Runner_data('2586674', 1886))The bib number and the total time taken by the runner is added to runners each time they cross the finish line.Now, you know the top five runners according to the duration time are the winners and the rest of them will be sorted by the fastest time:>>> runners.sort(key=lambda x: getattr(x, 'duration')) >>> fastest_five_runners = runners[:5]In this example, we didn’t need any multiple types of sorting. The list was a reasonable choice. You just sorted the participants and grabbed the fastest five runners. Storing the list elsewhere was also not needed. The lambda function is used here to get the duration of each runner and then sorting is performed. Finally, the result is stored in fastest_five_runners.However, the managing director of the race comes to you and informs that they have decided that every 20th runner will be awarded a free sports bag. Since the original data has been changed and cannot be recoverable, it is impossible to find every 20th runner.In such cases, where you find a slight possibility that the original data is to be recovered, use sorted() instead of sort().Let us implement the same code above using sorted():>>> runners_by_time = sorted(runners, key=lambda x: getattr(x, 'duration')) >>> fastest_five_runners = runners_by_time[:5]In this situation, sorted() holds the original list of runners and their data and is not overwritten. You can find every 20th person to cross the finish line by interacting with the original values:>>> every_twentieth_runner = runners[::20]List slice on runners is used to create  every_twentieth_runner that holds the actual order in which runners crossed the finish line.So, sorted() should be used in cases where the original data is to be retained and sort() should be used where the original data is a copy or unimportant and losing it won’t stand as an issue.Some Earlier ways of  Python SortingThere were mainly two approaches of sorting when Python 2 was released— decorated-sort-undecorated and using cmp parameter.Decorated-Sort-UndecoratedThis idiom Decorated-Sort-Undecorated is based upon three three steps:First of all, the original list is decorated with new elements which manages the sort order.Secondly, sorting is performed on the decorated list.Finally, a list is created that contains the original elements in the new order and the decorations are removed.Let us see an example of the DSU approach using a class:>>> class Student:       def prop(self,name, grade, age):           self.name = name           self.grade = grade           self.age = age       def stu_repr(self):           return repr((self.name, self.grade, self.age)) >>> student_objects = [       Student('alex', 'B', 13),       Student('bob', 'A', 12),       Student('chrles', 'B', 10),     ] #Regular sorting using sorted() >>> sorted(student_objects, key=lambda student: student.age) [('charles', 'B', 10), ('bob', 'A', 12), ('alex', 'B', 13)] #DSU Approach >>> decorated_values = [(student.grade, i, student) for i, student in enumerate(student_objects)] >>> decorated_values.sort() >>> [student for grade, i, student in decorated_values]   [('bob', 'A', 12), ('alex', 'B', 13),('charles', 'B', 10)]In this code above, a class Student is created with student objects name, grade and age. Firstly, the original values are decorated and then sorted. Finally, the decorations are removed from decorated_values and then the new list is created with original values in new order.The Decorated-Sort-Undecorated technique is also the Schwartzian Transform and is helpful in increasing the efficiency of sorting in Python.Using cmp Parametercmp is a method or  parameter in Python that is used to compare two arguments. It returns either of the three values– a negative value in case of less than (<) comparisons or zero if equal or a positive value for greater than (>) comparisons.An example to illustrate cmp using sorted():>>> def num_compare(a, b):       return a - b >>> sorted([9, 2, 5, 0, 7], cmp=num_compare) [0, 2, 5, 7, 9]Here, a function num_compare is created and then the list is sorted by comparing each value in the list. Finally, the output is displayed in ascending order.Note that cmp parameter will work only in Python 2 . It is completely removed from Python 3 to make the language more simple and to resist conflicts between other comparison techniques and cmp.SummaryLet us sum up what we have learned in this article so far—Sorting and its needs.How to use sorted() to sort values with and without key and reverse.How to use .sort() to order values with and without key and reverse.Limitations and Gotchas with Python Sorting.Appropriate use of .sort() and sorted().Both .sort() and sorted() can be used to sort elements in a similar manner if used properly with key and reverse arguments.However, both have different characteristics when output and in-place modifications are considered, so it is suggested to first have a clear understanding of the program to be worked upon, while using .sort() since it can irrevocably overwrite data.To become a good Python developer, understanding complex sorting algorithms would be a useful skill set in the long run. For more information about sorting in Python, look into the official documentation of sorting of the Python Software Foundation and also grab a glimpse of another Python sorting algorithm called the TimSort. You may also join our Python certification course to gain further skills and knowledge in Python.

How to use sorted() and sort() in Python

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How to use sorted() and sort() in Python

Whenever you visit a pharmacy and ask for a particular medicine, have you noticed something? It hardly takes any time for the pharmacist to find it among several medicines. This is because all the items are arranged in a certain fashion which helps them know the exact place to look for. They may be arranged in alphabetical order or according to their category such as ophthalmics or neuro or gastroenterology and so on. A proper arrangement not only saves time but also make operations simple and easy, hence sorting is essential.

At some point or the other, every programmer needs to learn one of the most essential skills,  Sorting. Python sorting functions comprise of a lot of features to perform basic sorting or customize ordering according to the programmer’s needs.

Basically, sorting is a technique that is used to arrange data in an increasing and decreasing fashion according to some linear relationship among the data elements. You can sort numbers, or names or records of any kind in any fashion according to your needs. Sorting techniques can be used to arrange a list of mail recipients in an alphabetical manner.

There are a number of sorting algorithms in Python starting from Bubble Sort, Insertion Sort, Quick Sort, Merge Sort, Selection Sort and so on. In this article we will look into how to use sorted() and sort() in Python. To learn more about other concepts of Python, go through our Python Tutorials.

What is the need for Sorting?

In simple terms, sorting means arranging data systematically. If the data you want to work with  is not sorted you will face problems in finding your desired element.

The main advantages of sorting elements in Python are:

  • When you work with sorting modules, you will get to know about a large number of language components.
  • Sorting Algorithms provide an abstract way of learning about the accuracy of your program without worrying about system developments or dependencies.
  • It will help you in understanding the program complexity and speed and how to increase the efficiency.

How to order values using sorted()?

sorted() is a built-in function that accepts an iterable and returns the sorted values in ascending order by default which contains the iterable items.

Sorting Numbers using sorted()

Let us define a list of integers called num_list and pass it as an argument to sorted():

>>> num_list = [4, 10, 1, 7]
>>> sorted(num_list)
[1, 4, 7, 10]
>>> num_list
[4, 10, 1, 7]

Some of the insights we gain from the code above are:

  • sorted() is a built-in function found in the Python Standard Library. It cannot be defined.
  • sorted() orders the values in num_list in ascending order by default, i.e. smallest to largest.
  • The original values of num_list are not changed.
  • sorted() being called, returns an ordered list of values.

Since sorted() function returns the list in order, we can assign the returned list to another variable:

>>> num_list = [4, 10, 1, 7]
>>> sorted_list = sorted(num_list)
>>> sorted_list
[1, 4, 7, 10]
>>> num_list
[4, 10, 1, 7]

A new variable sorted_list is created which holds the output of sorted().

You can also use sorted() to sort tuples and sets just like numbers:

>>> tuples = (4, 10, 1, 7)
>>> sets = {10, 5, 10, 0, 2}
>>> sorted_tuples = sorted(numbers_tuple)
>>> sorted_sets = sorted(numbers_set)
>>> sorted_tuples
[1, 4, 7, 10]
>>> sorted_sets
[0, 2, 5, 10]

The definition of sorted() states that it will return a new list whatever the input may be. So even if the input variables are tuples and sets, sorted() always returns a list.

You can also perform type casting in cases where you need to match the returned object with the input type:

>>> tuples = (4, 10, 1, 7)
>>> sets = {10, 5, 10, 0, 2}
>>> sorted_tuples = sorted(numbers_tuple)
>>> sorted_sets = sorted(numbers_set)
>>> sorted_tuples
[1, 4, 7, 10]
>>> sorted_sets
[0, 2, 5, 10]
>>> tuples(sorted_tuples)
(1, 4, 7, 10)
>>> sets(sorted_sets)
{0, 2, 5, 10}

In the code above, you can see the sorted_tuples when cast to tuples is retained in an ordered manner whereas sorted_sets when casted does not return an order list since it is unordered by definition.

Sorting Strings using sorted()

Sorting of strings is just like sorting tuples and sets. sorted() iterates across each character of the input and returns a string order.

An example of sorting str type using sorted():

>>> num_string = '98765'
>>> char_string = 'sorting is fun'
>>> sorted_num_string = sorted(num_string)
>>> sorted_char_string = sorted(char_string)
>>> sorted_num_string
['5', '6', '7', '8', '9']
>>> sorted_char_string
['', '','f', 'g', 'i', 'i', 'n', 'n', 'o', 'r', 's', 's', 't','u']

The str is treated as a list and sorted() iterates through each character including spaces.

You can use .split() to change the behavior and clean the output and .join() to rejoin them together:

>>> string = 'sorting is fun'
>>> sorted_string = sorted(string.split())
>>> sorted_string
['fun', 'is', 'sorting']
>>> ' '.join(sorted_string)
'fun is sorting'

The actual string is converted into a list of words using .split() and then it is sorted with sorted() and then again joined together using .join().

How to use sorted() with a reverse Argument?

The syntax of the sorted() function is sorted(iterable, /, *, key=None, reverse=False).

The built-in function sorted() comprises of three parameters:

  • iterable — Required. A sequence such as string, tuple or list and collection such as set or dictionary.
  • key — Optional. A function that serves as a key or to customize the sort order. The argument is set to None by default.
  • reverse — Optional. A boolean flag that reverses the order of sorting. If True, the sorted list is reversed. The default argument is False.

reverse is an optional keyword argument that changes the sorting order according to the Boolean value assigned to it. The default value is False, which performs sorting in ascending order. However, if the value is given as True, descending sort occurs:

>>> name_list = ['Markian', 'Alex', 'Suzzane', 'Harleen']
>>> sorted(name_list)
['Alex', 'Harleen', 'Markian', 'Suzzane']
>>> sorted(name_list, reverse=True)
['Suzzane', 'Markian', 'Harleen', 'Alex']

In the example above, the sorting is done on the basis of the first alphabet. However, when sorted() encounters the reverse keyword with a True argument, the output is reversed.

Another example to understand the behavior of the reverse keyword:

>>> case_sensitive_names = ['Markian', 'alex', 'Suzzane', 'harleen']
>>> sorted(case_sensitive_names, reverse=True)
['harleen', 'alex', 'Suzzane', 'Markian']
>>> values_to_sort = [False, 1, 'A' == 'B', 1 <= 0]
>>> sorted(values_to_sort, reverse=True)
[1, False, False, False]
>>> num_list = [7, 10, 0, 4]
>>> sorted(num_list, reverse=False)
[0, 4, 7, 10]

How to use sorted() with a key Argument?

The keyword argument key accepts a function and this function determines the resulting order by implementing itself in each value of the list.

An example to illustrate sorting of a list using the function len(), which returns the length of the string, and providing the key argument as len:

>>> word = 'pencil'
>>> len(word)
6
>>> word_list = ['cherry', 'donut', 'Michigan', 'transcipt']
>>> sorted(word_list, key=len)
['donut', 'cherry', 'Michigan', 'transcript']

The len() function determines the length of each item in the list and returns the list in ascending order (shortest to longest).

Let us sort the earlier example using key where the first alphabet with different case was considered for the order:

>>> case_sensitive_names = ['Markian', 'alex', 'Suzzane', 'harleen']
>>> sorted(case_sensitive_names, reverse=True)
['Markian', 'Suzzane', 'alex', 'harleen']
>>> sorted(case_sensitive_names, key=str.lower)
['alex', 'harleen', 'Markian', 'Suzzane']

The key cannot make any changes to the original values in the list. So the final output will be the original sorted elements.

Though key is considered as one of the most powerful components of sorted(), it has a number of limitations.

The first limitation is that key accepts only single argument functions.

An example of a function addition that accepts two arguments:

>>> def addition(a, b):
      return a + b
>>> number_to_add = [1, 3, 5]
>>> sorted(number_to_add , key=addition)
Traceback (most recent call last):
  File "stdin", line 5, in <module>
    sorted(number_to_add, key=addition)
TypeError: addition() missing 1 required positional argument: 'b'

The program fails because whenever addition() is called during sorting, it receives only one element from the list at a time. The second argument is always missing.

The second limitation is that the key function that is used must be able to handle all types of iterable values.

An example to illustrate the second limitation:

>>> cast_values = ['4', '5', '6', 'seven']
>>> sorted(cast_values, key=int)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: invalid literal for int() with base 10: 'seven'

The example above contains a list of numbers to be used by sorted() as strings. The key will try to convert the numbers to int. Each of the numbers represented as strings can be converted to int, but four cannot be. So a ValueError gets raised since four is not valid to cast into an int.

Let us see an example to arrange an iterable by the last letter of each string:

>>> def reverse(word):
      return word[::-1]
>>> words = ['cherry', 'cake', 'Michigan', 'transcript']
>>> sorted(words, key=reverse)
['cake', 'Michigan', 'transcript', 'cherry']

The function reverse is defined to reverse the input string and then the function is used as the key argument. The slice syntax word[::-1] reverses the string and then the function reverse() takes all the elements one at a time and sorts the list according to the last alphabet.

You can also use lambda function in the key argument instead of defining a regular function. A lambda is an anonymous function that does not have a name and executes just like normal functions. Lambda functions do not contain any statements.

An example to show the previous code using a lambda function:

>>> words = ['cherry', 'cake', 'Michigan', 'transcript']
>>> sorted(words, key = lambda x: x[::-1])
['cake', 'Michigan', 'transcript', 'cherry']

Here, the key is defined with lambda with no name and x is the argument. The slice syntax word[::-1] reverses each of the element and the reversed output is then used for sorting.

An example to use key along with reverse argument:

>>> words = ['cherry', 'cake', 'Michigan', 'transcript']
>>> sorted(words, key = lambda x: x[::-1], reverse = True)
['cherry', 'transcript', 'Michigan', 'cake']

In this example, the order is reversed into a descending manner.

Lambda functions can also be used to sort class objects according to their properties.

An example to sort a group of students based on their grade in descending order:

>>> from collections import namedtuple
>>> Student = namedtuple('Student', 'name grade')
>>> alex = Student('Alex', 95)
>>> bob = Student('Bob', 87)
>>> charlie = Student('Charlie', 91)
>>> students = [alex, bob, charlie]
>>> sorted(students, key=lambda x: getattr(x, 'grade'), reverse=True)
[Student(name='Alex', grade=95), Student(name='Charlie', grade=91), Student(name='Bob', grade=87)]

The namedtuple is used to produce classes with name and grade attributes. The lambda is used to get the grade property of each student and reverse is used to reverse the output into descending order so that the highest grades are arranged first.

There are a lot of possible techniques to arrange elements using sorted() with key and reverse arguments. Lambda functions can also be helpful during sorting by making your code simple and clean.

You can also use operator module functions like itemgetter() and attrgetter() to make your sorting program simpler and faster. The operator module is used to export a set of accessor functions in correspondence to the operators of Python.

An example to illustrate operator module functions using key:

>>> tuples = [
      ('alex', 'B', 13),
      ('bob', 'A', 12),
      ('charles', 'B', 10),
      ]
>>> from operator import itemgetter
>>> sorted(tuples, key=itemgetter(2))
>>>[('charles', 'B', 10), ('bob', 'B', 12), ('alex', 'A', 13)]

tuples is declared with the name, grade and age of three persons. The function itemgetter is imported from the module operator and then it is sorted by age and the output displayed in ascending order.

How to order values using sort()?

The .sort() which is quite similar to sorted() in naming has few differences than sorted(). The help documentation of Python will clear out the two critical differences between .sort() and sorted():

>>> help(sorted)
Help on built-in function sorted in module builtins:
sorted(iterable, /, *, key=None, reverse=False)
    Return a new list containing all items from the iterable in ascending order.
    A custom key function can be supplied to customize the sort order, and the
    reverse flag can be set to request the result in descending order.
>>> help(list.sort)
Help on method_descriptor:
sort(self, /, *, key=None, reverse=False)
    Stable sort *IN PLACE*.

Firstly, .sort() is not a built-in function unlike sorted(). It is a method of list class and works only with lists. You cannot pass iterables to .sort().

Secondly, .sort()  returns None and changes the values.

Let us see the differences of code for .sort() and what impact it has on the code:

>>> sort_numbers = [10, 2, 7, 3]
>>> sort(sort_numbers)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'sort' is not defined
>>> sort_tuples = (10, 2, 7, 3)
>>> sort_tuple.sort()>>> sort_tuples = (10, 2, 7, 3)
>>> sort_tuple.sort()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'tuple' object has no attribute 'sort'
>>> sorted_values = sort_numbers.sort()
>>> print(sorted_values)
None
>>> sorted_values = sort_numbers.sort()
>>> print(sorted_values)int(sort_numbers)
[1, 2, 5, 6]

The code above highlights some operational differences between .sort() and sorted():

  • When any assignment is done to a new variable, it returns a None type. This is because .sort() function has no ordered output. 
  • The original order of sort_numbers is not maintained and is changed in place.

.sort() also contains the key and reverse optional keyword arguments just like sorted() which produces the same functionality.

An example of .sort() using lambda to sort a list of phrases by the first letter of the third word:

>>> sort_phrases = ['welcome to python',
      'python is fun',
      'python is easy'
      ]
>>> sort_phrases.sort(key=lambda x: x.split()[2][1], reverse=False)
>>> sort_phrases
['python is easy', 'python is fun', 'welcome to python']

Here, lambda is used to split each phrase into a list of words and then find the second letter of the third element for each phrase.

Disadvantages of  Python Sorting

Python has some limitations when you try to sort values besides integers.

Non-Comparable Data Types

You cannot use sort data types that are different from each other. Python raises an error when sorted() is used on non-comparable data.

An example to illustrate sorting of values of different data types:

>>> mixed_values = [None, 5]
>>> sorted(mixed_values)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'int' and 'NoneType'

Python raises a TypeError because it cannot sort None and int in the same list because of their incompatibility. It uses the less than operator ( < ) to determine the lower value in the order of the sort.

If you try to compare the same values manually without using sorted(), it will still raise a TypeError because of non-comparable data types:

>> None < 5
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'NoneType' and 'int'

However, if your list contains a combination of integers and strings that are all numbers, Python will cast them to comparable types using a list comprehension:

>>> num_mix = [10, "5", 200, "11"]
>>> sorted(num_mix)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'str' and 'int'
>>> # List comprehension to cast all values to integers
>>> [int(z) for z in num_mix]
[10, 5, 200, 11]
>>> sorted([int(z) for z in num_mix])
[5, 10, 11, 200]

int() converts all the string values in num_mix to integers and then sorted() compares all values and returns a sorted output.

An example of a Python code of implicitly converting a value to another type:

>>> values = [1, False, 0, 'a' == 'b', 0 >= 1]
>>> sorted(values)
[False, 0, False, False, 1]

In the example above, all the elements in the list are converted to boolean type. 0 >= 1 evaluates to a False output. The number 1 and 0 are converted to True and False as bool type respectively.

This particular example highlights an important characteristic of sorting– sort stability. Sorting ability means that sorting algorithms are always stable. The original order is retained even if multiple records have the same key argument.

An example to illustrate sort stability:

>>> values = [False, 0, 0, 3 == 4, 1, False, False]
>>> sorted(values)
[False, 0, 0, False, 0, False, 1]

If you take a look at the original order and the sorted output, you’ll find that the expression 3 == 4 is casted to False and all sorted output is in the actual order. You can also perform complex sorts with the help of the knowledge of sort stability.

Case-Sensitive Sorting

You can use sorted() to sort a list of strings in ascending order which is alphabetical by default:

>>> name_list = ['Markian', 'Alex', 'Suzzane', 'Harleen']
>>> sorted(name_list)
['Alex', 'Harleen', 'Markian', 'Suzzane']

However, Python uses Unicode Code Point of the first letter of each string to evaluate the ascending order of the sort. If there are two names Al and al, Python will treat both of them differently.

An example to return the Unicode Code Point of the first alphabet of each string:

>>> case_sensitive_names = ['Markian', 'alex', 'Suzzane', 'harleen']
>>> sorted(case_sensitive_names)
['Markian', 'Suzzane', 'alex', 'harleen']
>>> # List comprehension for Unicode Code Point of first letter in each word
>>> [(ord(name[0]), name[0]) for name in sorted(case_sensitive_names)]
[(77, 'M'), (83, 'S'), (97, 'a'), (104, 'h')]

In the example above, name[0] returns the first letter of the string and ord(name[0]) returns the Unicode Code Point. You can notice that even a comes before M alphabetically, the output has M before a. This is because the code point of M comes before a.

Consider a situation where the first letter is the same for all the strings that need to be sorted. In such cases, the sorted() function will use the second letter to determine the order and if the second letter is also same, it will consider the third letter and so on, till the end of string:

>>> similar_strings = ['zzzzzn', 'zzzzzc', 'zzzzza','zzzzze']
>>> sorted(similar_strings)
['zzzzza', 'zzzzzc', 'zzzzze', 'zzzzzn']

Here, sorted() will compare the strings based on the sixth character since the first five characters are the same ( z ). The output will also depend on the last character of each string.

An example of sorting elements having identical values:

>>> different_lengths = ['zzzzz', 'zz', 'zzzz','z']
>>> sorted(different_lengths)
['z', 'zz', 'zzzz', 'zzzzz']

In this case, the sorting order will be from the shortest to the longest. The shortest string z is ordered first and the longest string zzzzz is ordered at the last.

When should you use .sort() and sorted()?

Let us consider a case where you need to collect data from a race of 5k runners, the Python 5k Annual and then sort them. You will have to collect the runner’s bib number and the time it took to finish the race:

>>> from collections import namedtuple
>>> Runner_data = namedtuple('Runner', 'bibnumber duration')

Each of the Runner_data will be added to a list called runners:

>>> runners = []
>>> runners.append(Runner_data('2548597', 1200))
>>> runners.append(Runner_data('8577724', 1720))
>>> runners.append(Runner_data('2666234', 1600))
>>> runners.append(Runner_data('2425114', 1450))
>>> runners.append(Runner_data('2235232', 1620))
    ...
    ...
>>> runners.append(Runner_data('2586674', 1886))

The bib number and the total time taken by the runner is added to runners each time they cross the finish line.

Now, you know the top five runners according to the duration time are the winners and the rest of them will be sorted by the fastest time:

>>> runners.sort(key=lambda x: getattr(x, 'duration'))
>>> fastest_five_runners = runners[:5]

In this example, we didn’t need any multiple types of sorting. The list was a reasonable choice. You just sorted the participants and grabbed the fastest five runners. Storing the list elsewhere was also not needed. The lambda function is used here to get the duration of each runner and then sorting is performed. Finally, the result is stored in fastest_five_runners.

However, the managing director of the race comes to you and informs that they have decided that every 20th runner will be awarded a free sports bag. Since the original data has been changed and cannot be recoverable, it is impossible to find every 20th runner.

In such cases, where you find a slight possibility that the original data is to be recovered, use sorted() instead of sort().

Let us implement the same code above using sorted():

>>> runners_by_time = sorted(runners, key=lambda x: getattr(x, 'duration'))
>>> fastest_five_runners = runners_by_time[:5]

In this situation, sorted() holds the original list of runners and their data and is not overwritten. You can find every 20th person to cross the finish line by interacting with the original values:

>>> every_twentieth_runner = runners[::20]

List slice on runners is used to create  every_twentieth_runner that holds the actual order in which runners crossed the finish line.

So, sorted() should be used in cases where the original data is to be retained and sort() should be used where the original data is a copy or unimportant and losing it won’t stand as an issue.

Some Earlier ways of  Python Sorting

There were mainly two approaches of sorting when Python 2 was released— decorated-sort-undecorated and using cmp parameter.

Decorated-Sort-Undecorated

This idiom Decorated-Sort-Undecorated is based upon three three steps:

  • First of all, the original list is decorated with new elements which manages the sort order.
  • Secondly, sorting is performed on the decorated list.
  • Finally, a list is created that contains the original elements in the new order and the decorations are removed.

Let us see an example of the DSU approach using a class:

>>> class Student:
      def prop(self,name, grade, age):
          self.name = name
          self.grade = grade
          self.age = age
      def stu_repr(self):
          return repr((self.name, self.grade, self.age))
>>> student_objects = [
      Student('alex', 'B', 13),
      Student('bob', 'A', 12),
      Student('chrles', 'B', 10),
    ]
#Regular sorting using sorted()
>>> sorted(student_objects, key=lambda student: student.age)
[('charles', 'B', 10), ('bob', 'A', 12), ('alex', 'B', 13)]
#DSU Approach
>>> decorated_values = [(student.grade, i, student) for i, student in enumerate(student_objects)]
>>> decorated_values.sort()
>>> [student for grade, i, student in decorated_values]   [('bob', 'A', 12), ('alex', 'B', 13),('charles', 'B', 10)]

In this code above, a class Student is created with student objects name, grade and age. Firstly, the original values are decorated and then sorted. Finally, the decorations are removed from decorated_values and then the new list is created with original values in new order.

The Decorated-Sort-Undecorated technique is also the Schwartzian Transform and is helpful in increasing the efficiency of sorting in Python.

Using cmp Parameter

cmp is a method or  parameter in Python that is used to compare two arguments. It returns either of the three values– a negative value in case of less than (<) comparisons or zero if equal or a positive value for greater than (>) comparisons.

An example to illustrate cmp using sorted():

>>> def num_compare(a, b):
      return a - b
>>> sorted([9, 2, 5, 0, 7], cmp=num_compare)
[0, 2, 5, 7, 9]

Here, a function num_compare is created and then the list is sorted by comparing each value in the list. Finally, the output is displayed in ascending order.

Note that cmp parameter will work only in Python 2 . It is completely removed from Python 3 to make the language more simple and to resist conflicts between other comparison techniques and cmp.

Summary

Let us sum up what we have learned in this article so far—

  • Sorting and its needs.
  • How to use sorted() to sort values with and without key and reverse.
  • How to use .sort() to order values with and without key and reverse.
  • Limitations and Gotchas with Python Sorting.
  • Appropriate use of .sort() and sorted().

Both .sort() and sorted() can be used to sort elements in a similar manner if used properly with key and reverse arguments.

However, both have different characteristics when output and in-place modifications are considered, so it is suggested to first have a clear understanding of the program to be worked upon, while using .sort() since it can irrevocably overwrite data.

To become a good Python developer, understanding complex sorting algorithms would be a useful skill set in the long run. For more information about sorting in Python, look into the official documentation of sorting of the Python Software Foundation and also grab a glimpse of another Python sorting algorithm called the TimSort. You may also join our Python certification course to gain further skills and knowledge in Python.

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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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

Programming is at the core of software development... Read More

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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