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How To Write Beautiful Python Code With PEP 8

It gets difficult to understand a messed up handwriting, similarly an unreadable and unstructured code is not accepted by all. However, you can benefit as a programmer only when you can express better with your code. This is where PEP comes to the rescue. Python Enhancement Proposal or PEP is a design document which provides information to the Python community and also describes new features and document aspects, such as style and design for Python.Python is a multi-paradigm programming language which is easy to learn and has gained popularity in the fields of Data Science and Web Development over a few years and PEP 8 is called the style code of Python. It was written by Guido van Rossum, Barry Warsaw, and Nick Coghlan in the year 2001. It focuses on enhancing Python’s code readability and consistency. Join the certification course on Python Programming and gain skills and knowledge about various features of Python along with tips and tricks.A Foolish Consistency is the Hobgoblin of Little Minds‘A great person does not have to think consistently from one day to the next’ — this is what the statement means.Consistency is what matters. It is considered as the style guide. You should maintain consistency within a project and mostly within a function or module.However, there will be situations where you need to make use of your own judgement, where consistency isn’t considered an option. You must know when you need to be inconsistent like for example when applying the guideline would make the code less readable or when the code needs to comply with the earlier versions of Python which the style guide doesn’t recommend. In simple terms, you cannot break the backward compatibility to follow with PEP.The Zen of PythonIt is a collection of 19 ‘guiding principles’ which was originally written by Tim Peters in the year 1999. It guides the design of the Python Programming Language.Python was developed with some goals in mind. You can see those when you type the following code and run it:>>> import this The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those!The Need for PEP 8Readability is the key to good code. Writing good code is like an art form which acts as a subjective topic for different developers.Readability is important in the sense that once you write a code, you need to remember what the code does and why you have written it. You might never write that code again, but you’ll have to read that piece of code again and again while working in a project. PEP 8 adds a logical meaning to your code by making sure your variables are named well, sufficient whitespaces are there or not and also by commenting well. If you’re a beginner to the language, PEP 8 would make your coding experience more pleasant.Following PEP 8 would also make your task easier if you’re working as a professional developer. People who are unknown to you and have never seen how you style your code will be able to easily read and understand your code only if you follow and recognize a particular guideline where readability is your de facto.And as Guido van Rossum said— “Code is read much more than it is often written”.The Code LayoutYour code layout has a huge impact on the readability of your code.IndentationThe indentation level of line is computed by the leading spaces and tabs at the beginning of a line of logic. It influences the grouping of statements. The rules of PEP 8 says to use 4 spaces per indentation level and also spaces should be preferred over tabs.An example of code to show indentation:x = 5 if x < 10:   print('x is less than 10') Tabs or Spaces?Here the print statement is indented which informs Python to execute the statement only if the if statement is true. Indentation also helps Python to know what code it will execute during function calls and also when using classes.PEP 8 recommends using 4 spaces to show indentation and tabs should only be used to maintain consistency in the code.Python 3 forbids the mixing of spaces and tabs for indentation. You can either use tabs or spaces and you should maintain consistency while using Python 3. The errors are automatically displayed:python hello.py  File "hello.py", line 3       print(i, j)                 ^TabError: inconsistent use of tabs and spaces in indentationHowever, if you’re working in Python 2, you can check the consistency by using a -t flag in your code which will display the warnings of inconsistencies with the use of spaces and tabs.You can also use the -tt flag which will show the errors instead of warnings and also the location of inconsistencies in your code. Maximum Line Length and Line BreakingThe Python Library is conservative and 79 characters are the maximum required line limit as suggested by PEP 8. This helps to avoid line wrapping.Since maintaining the limit to 79 characters isn’t always possible, so PEP 8 allows wrapping lines using Python’s implied line continuation with parentheses, brackets, and braces:def function(argument_1, argument_2,             argument_3, argument_4):     return argument_1Or by using backslashes to break lines:with open('/path/to/some/file/you/want/to/read') as example_1, \     open('/path/to/some/file/being/written', 'w') as example_2:     file_2.write(file_1.read())When it comes to binary operators, PEP 8 encourages to break lines before the binary operators. This accounts for more readable code.Let us understand this by comparing two examples:# Example 1 # Do total = ( variable_1 + variable_2 - variable_3 ) # Example 2 # Don't total = ( variable_1 + variable_2 - variable_3 )In the first example, it is easily understood which variable is added or subtracted, since the operator is just next to the variable to which it is operated. However, in the second example, it is a little difficult to understand which variable is added or subtracted.Indentation with Line BreaksIndentation allows a user to differentiate between multiple lines of code and a single line of code that spans multiple lines. It enhances readability too.The first style of indentation is to adjust the indented block with the delimiter:def function(argument_one, argument_two,               argument_three, argument_four):         return argument_oneYou can also improve readability by adding comments:x = 10 if (x > 5 and     x < 20):     # If Both conditions are satisfied     print(x)Or by adding extra indentation:x = 10 if (x > 5 and       x < 20):     print(x)Another type of indentation is the hanging indentation by which you can symbolize a continuation of a line of code visually:foo = long_function_name(       variable_one, variable_two,       variable_three, variable_four)You can choose any of the methods of indentation, following line breaks, in situations where the 79 character line limit forces you to add line breaks in your code, which will ultimately improve the readability.Closing Braces in Line ContinuationsClosing the braces after line breaks can be easily forgotten, so it is important to put it somewhere where it makes good sense or it can be confusing for a reader.One way provided by PEP 8 is to put the closing braces with the first white-space character of the last line:my_list_of_numbers = [     1, 2, 3,     4, 5, 6,     7, 8, 9     ]Or lining up under the first character of line that initiates the multi-line construct:my_list_of_numbers = [     1, 2, 3,     4, 5, 6,     7, 8, 9 ]Remember, you can use any of the two options keeping in mind the consistency of the code.Blank LinesBlank lines are also called vertical whitespaces. It is a logical line consisting of spaces, tabs, formfeeds or comments that are basically ignored.Using blank lines in top-level-functions and classes:class my_first_class:     pass class my_second_class:     pass def top_level_function():     return NoneAdding two blank lines between the top-level-functions and classes will have a clear separation and will add more sensibility to the code.Using blank lines in defining methods inside classes:class my_class:     def method_1(self):         return None     def method_2(self):         return NoneHere, a single vertical space is enough for a readable code.You can also use blank spaces inside multi-step functions. It helps the reader to gather the logic of your function and understand it efficiently. A single blank line will work in such case.An example to illustrate such:def calculate_average(number_list):     sum_list = 0     for number in number_list:         sum_list = sum_list + number         average = 0     average = sum_list / len(number_list)    return averageAbove is a function to calculate the average. There is a blank line between each step and also before the return statement.The use of blank lines can greatly improve the readability of your code and it also allows the reader to understand the separation of the sections of code and the relation between them.Naming ConventionsChoosing names which are sensible and can be easily understandable, while coding in Python, is very crucial. This will save time and energy of a reader. Inappropriate names might lead to difficulties when debugging.Naming StylesNaming variables, functions, classes or methods must be done very carefully. Here’s a list of the type, naming conventions and examples on how to use them:TypeNaming ConventionsExamplesVariableUsing short names with CapWords.T, AnyString, My_First_VariableFunctionUsing a lowercase word or words with underscores to improve readability.function, my_first_functionClassUsing CapWords and do not use underscores between words.Student, MyFirstClassMethodUsing lowercase words separated by underscores.Student_method, methodConstantsUsing all capital letters with underscores separating wordsTOTAL, MY_CONSTANT, MAX_FLOWExceptionsUsing CapWords without underscores.IndexError, NameErrorModuleUsing short lower-case letters using underscores.module.py, my_first_module.pyPackageUsing short lowercase words and underscores are discouraged.package, my_first_packageChoosing namesTo have readability in your code, choose names which are descriptive and give a clearer sense of what the object represents. A more real-life approach to naming is necessary for a reader to understand the code.Consider a situation where you want to store the name of a person as a string:>>> name = 'John William' >>> first_name, last_name = name.split() >>> print(first_name, last_name, sep='/ ') John/ WilliamHere, you can see, we have chosen variable names like first_name and last_name which are clearer to understand and can be easily remembered. We could have used short names like x, y or z but it is not recommended by PEP 8 since it is difficult to keep track of such short names.Consider another situation where you want to double a single argument. We can choose an abbreviation like db for the function name:# Don't def db(x):     return x * 2However, abbreviations might be difficult in situations where you want to return back to the same code after a couple of days and still be able to read and understand. In such cases, it’s better to use a concise name like double_a_variable:# Do def double_a_value(x):     return x * 2Ultimately, what matters is the readability of your code.CommentsA comment is a piece of code written in simple English which improves the readability of code without changing the outcome of a program. You can understand the aim of the code much faster just by reading the comments instead of the actual code. It is important in analyzing codes, debugging or making a change in logic. Block CommentsBlock comments are used while importing data from files or changing a database entry where multiples lines of code are written to focus on a single action. They help in interpreting the aim and functionality of a given block of code.They start with a hash(#) and a single space and always indent to the same level as the code:for i in range(0, 10):     # Loop iterates 10 times and then prints i     # Newline character     print(i, '\n')You can also use multiple paragraphs in a block comment while working on a more technical program. Block comments are the most suitable type of comments and you can use it anywhere you like.Inline CommentsInline comments are the comments which are placed on the same line as the statement. They are helpful in explaining why a certain line of code is essential.Example of inline comments:x = 10  # An inline comment y = 'JK Rowling' # Author NameInline comments are more specific in nature and can easily be used which might lead to clutter. So, PEP 8 basically recommends using block comments for general-purpose coding.Document StringsDocument strings or docstrings start at the first line of any function, class, file, module or method. These type of comments are enclosed between single quotations ( ''') or double quotations ( """ ).An example of docstring:def quadratic_formula(x, y, z, t):     """Using the quadratic formula"""     t_1 = (- b+(b**2-4*a*c)**(1/2)) / (2*a)     t_2 = (- b-(b**2-4*a*c)**(1/2)) / (2*a)     return t_1, t_2Whitespaces in Expressions and StatementsIn computing, whitespace is any character or sequence of characters which are used for spacing and have an ‘empty’ representation. It is helpful in improving the readability of expressions and statements if used properly.Whitespace around Binary OperatorsWhen you’re using assignment operators ( =, +=, -=,and so forth ) or comparisons ( ==, !=, >, <. >=, <= ) or booleans ( and, not, or ), it is suggested to use a single whitespace on the either side.Example of adding whitespace when there is more than one operator in a statement:# Don't b = a ** 2 + 10 c = (a + b) * (a - b) # Do b = a**2 + 10 c = (a+b) * (a-b)In such mathematical computations, you should add whitespace around the operators with the least priority since adding spaces around each operator might be confusing for a reader.Example of adding whitespaces in an if statement with many conditions:# Don't if a < 10 and a % 5 == 0:     print('a is smaller than 10 and is divisible by 5!') # Do if a<10 and a%5==0:     print('a is smaller than 10 and is divisible by 5!')Here, the and operator has the least priority, so whitespaces have been added around it.Colons act as binary operators in slices:ham[3:4]ham[x+1 : x+2]ham[3:4:5]ham[x+1 : x+2 : x+3]ham[x+1 : x+2 :]Since colons act as a binary operator, whitespaces are added on either side of the operator with the lowest priority. Colons must have the same amount of spacing in case of an extended slice. An exception is when the slice parameter is omitted, space is also omitted.Avoiding WhitespacesTrailing whitespaces are whitespaces placed at the end of a line. These are the most important to avoid. You should avoid whitespaces in the following cases—Inside a parentheses, brackets, or braces:# Do list = [1, 2, 3] # Don't list = [ 1, 2, 3, ]Before a comma, a semicolon, or a colon:x = 2 y = 3 # Do print(x, y) # Don't print(x , y)Before open parenthesis that initiates the argument list of a function call:def multiply_by_2(a):       return a * 2 # Do multiply_by_2(3) # Don't multiply_by_2 (3)Before an open bracket that begins an index or a slice:# Do ham[5] # Don't ham [5]Between a trailing comma and a closing parenthesis:# Do spam = (1,) # Don't spam = (1, )To adjust assignment operators:# Do variable_1 = 5 variable_2 = 6 my_long_var = 7 # Don't variable_1    = 5 variable_2    = 6 my_long_var  = 7Programming RecommendationsPEP 8 guidelines suggest different ways to maintain consistency among multiple implementations of Python like PyPy, Jython or Cython.An example of comparing boolean values:# Don't bool_value = 5 > 4 if bool_value == True: return '4 is smaller than 5' # Do if bool_value: return '4 is smaller than 5'Since bool can only accept values True or False, it is useless to use the equivalence operator == in these type of if executions. PEP 8 recommends the second example which will require lesser and simpler coding. An example to check whether a list is empty or not:# Don't list_value = [] if not len(list_value):     print('LIST IS EMPTY') # Do list_value = [] if not list_value:     print('LIST IS EMPTY')Any empty list or string in Python is falsy. So you can write a code to check an empty string without checking the length of the list. The second example is more simple, so PEP encourages to write an if statement in this way.The expression is not and not ... is are identical in functionality. But the former is more preferable due to its nature of readability:# Do if x is not None:     return 'x has a value' # Don't if not x is None:     return 'x has a value'String slicing is a type of indexing syntax that extracts substrings from a string. Whenever you want to check if a string is prefixed or suffixed, PEP recommends using .startswith() and .endswith() instead of list slicing. This is because they are cleaner and have lesser chances of error:# Do if foo.startswith('cat'): # Don't if foo[:3] == 'cat':An example using .endswith():# Don't if file_jpg[-3:] == 'jpg':     print('It is a JPEG image file') # Do if file_jpg.endswith('jpg'):     print('It is a JPEG image file')Though there exists multiple ways to execute a particular action, the main agenda of the guidelines laid by PEP 8 is simplicity and readability.When to Ignore PEP 8You should never ignore PEP 8. If the guidelines related to PEP8 are followed, you can be confident of writing readable and professional codes. This will also make the lives of your  colleagues and other members working on the same project much easier. There are some exclusive instances when you may ignore a particular guideline:After following the guidelines, the code becomes less readable, even for a programmer who is comfortable with reading codes that follow PEP 8.If the surrounding code is inconsistent with PEP.Compatible of code with older version of Python is the priority.Checking PEP 8 Compliant CodeYou can check whether your code actually complies with the rules and regulations of PEP 8 or not. Linters and Autoformatters are two classes of tools used to implement and check PEP 8 compliance.LintersIt is a program that analyzes your code and detects program errors, syntax errors, bugs and structural problems. They also provide suggestions to correct the errors.Some of the best linters used for Python code:pycodestyle is a tool to verify the PEP 8 style conventions in your Python code.You can run the following from the command line to install pycodestyle using pip:pip install pycodestyleTo display the errors of a program, run pycodestyle in this manner:pycodestyle my_code.pymy_code.py:1:11: E231 missing whitespace after '{'my_code.py:3:19: E231 missing whitespace after ')'my_code.py:4:31: E302 expected 2 blank lines, found 1flake8 is a Python wrapper that verifies PEP 8, pyflakes, and circular complexity.Type the command to install flake8 using pip:pip install flake8Run flake8 from the terminal using the command:flake8 calc.py calc.py:24:3: E111 indentation is not a multiple of twocalc.py:25:3: E111 indentation is not a multiple of twocalc.py:45:9: E225 missing whitespace around operatorYou can also use some other good linters like pylint, pyflakes, pychecker and mypy.AutoformattersAn autoformatter is a tool which will format your code to adapt with PEP 8 automatically.One of the most commonly used autoformatter is black.To install black using pip, type:pip install blackRemember, you need to have Python 3.6 or above to install black.An example of code that doesn’t follow PEP 8:def add(a, b): return a+b def multiply(a, b):       return \         a   * bNow run black following the filename from the terminal:black my_code.pyreformatted my_code.pyAll done! The reformatted code will look like:def add(a, b):     return a + b def multiply(a, b):     return a * bSome other autoformatters include autopep8 and yapf. Their work is similar to black.ConclusionSince you have now learnt to write a good-quality and readable Python code using PEP 8, you’ll consider it a bliss while working in a project. Though it might be too precise in its nature, it will be useful to everyone working in a particular project by making the code more understandable and making changes and debugging easier.Let us sum up what we’ve learnt so far:What is PEP 8 and what is its importance.Multiple guidelines for writing PEP 8 compliant code.How to check code against PEP 8 using linters and autoformatters.If you intend to know more about PEP 8 and its book of guidelines, you can refer to pep8.org or simply enroll for the Python certification course offered by KnowledgeHut.

How To Write Beautiful Python Code With PEP 8

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How To Write Beautiful Python Code With PEP 8

It gets difficult to understand a messed up handwriting, similarly an unreadable and unstructured code is not accepted by all. However, you can benefit as a programmer only when you can express better with your code. This is where PEP comes to the rescue. 

Python Enhancement Proposal or PEP is a design document which provides information to the Python community and also describes new features and document aspects, such as style and design for Python.

Python is a multi-paradigm programming language which is easy to learn and has gained popularity in the fields of Data Science and Web Development over a few years and PEP 8 is called the style code of Python. It was written by Guido van Rossum, Barry Warsaw, and Nick Coghlan in the year 2001. It focuses on enhancing Python’s code readability and consistency. Join the certification course on Python Programming and gain skills and knowledge about various features of Python along with tips and tricks.

A Foolish Consistency is the Hobgoblin of Little Minds

‘A great person does not have to think consistently from one day to the next’ — this is what the statement means.

Consistency is what matters. It is considered as the style guide. You should maintain consistency within a project and mostly within a function or module.

However, there will be situations where you need to make use of your own judgement, where consistency isn’t considered an option. You must know when you need to be inconsistent like for example when applying the guideline would make the code less readable or when the code needs to comply with the earlier versions of Python which the style guide doesn’t recommend. 

In simple terms, you cannot break the backward compatibility to follow with PEP.

The Zen of Python

It is a collection of 19 ‘guiding principles’ which was originally written by Tim Peters in the year 1999. It guides the design of the Python Programming Language.

Python was developed with some goals in mind. You can see those when you type the following code and run it:

>>> import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

The Need for PEP 8

Readability is the key to good code. Writing good code is like an art form which acts as a subjective topic for different developers.

Readability is important in the sense that once you write a code, you need to remember what the code does and why you have written it. You might never write that code again, but you’ll have to read that piece of code again and again while working in a project. 

PEP 8 adds a logical meaning to your code by making sure your variables are named well, sufficient whitespaces are there or not and also by commenting well. If you’re a beginner to the language, PEP 8 would make your coding experience more pleasant.

Following PEP 8 would also make your task easier if you’re working as a professional developer. People who are unknown to you and have never seen how you style your code will be able to easily read and understand your code only if you follow and recognize a particular guideline where readability is your de facto.

And as Guido van Rossum said— “Code is read much more than it is often written”.

The Code Layout

Your code layout has a huge impact on the readability of your code.

Indentation

The indentation level of line is computed by the leading spaces and tabs at the beginning of a line of logic. It influences the grouping of statements. 

The rules of PEP 8 says to use 4 spaces per indentation level and also spaces should be preferred over tabs.

An example of code to show indentation:

x = 5
if x < 10:
  print('x is less than 10') 

Tabs or Spaces?

Here the print statement is indented which informs Python to execute the statement only if the if statement is true. Indentation also helps Python to know what code it will execute during function calls and also when using classes.

PEP 8 recommends using 4 spaces to show indentation and tabs should only be used to maintain consistency in the code.

Python 3 forbids the mixing of spaces and tabs for indentation. You can either use tabs or spaces and you should maintain consistency while using Python 3. The errors are automatically displayed:

python hello.py
  File "hello.py", line 3
      print(i, j)
                ^

TabError: inconsistent use of tabs and spaces in indentation

However, if you’re working in Python 2, you can check the consistency by using a -t flag in your code which will display the warnings of inconsistencies with the use of spaces and tabs.

You can also use the -tt flag which will show the errors instead of warnings and also the location of inconsistencies in your code. 

Maximum Line Length and Line Breaking

The Python Library is conservative and 79 characters are the maximum required line limit as suggested by PEP 8. This helps to avoid line wrapping.

Since maintaining the limit to 79 characters isn’t always possible, so PEP 8 allows wrapping lines using Python’s implied line continuation with parentheses, brackets, and braces:

def function(argument_1, argument_2,
            argument_3, argument_4):
    return argument_1

Or by using backslashes to break lines:

with open('/path/to/some/file/you/want/to/read') as example_1, \
    open('/path/to/some/file/being/written', 'w') as example_2:
    file_2.write(file_1.read())

When it comes to binary operators, PEP 8 encourages to break lines before the binary operators. This accounts for more readable code.

Let us understand this by comparing two examples:

# Example 1
# Do
total = ( variable_1 + variable_2 - variable_3 )
# Example 2
# Don't
total = ( variable_1 + variable_2 - variable_3 )

In the first example, it is easily understood which variable is added or subtracted, since the operator is just next to the variable to which it is operated. However, in the second example, it is a little difficult to understand which variable is added or subtracted.

Indentation with Line Breaks

Indentation allows a user to differentiate between multiple lines of code and a single line of code that spans multiple lines. It enhances readability too.

The first style of indentation is to adjust the indented block with the delimiter:

def function(argument_one, argument_two,
              argument_three, argument_four):
        return argument_one

You can also improve readability by adding comments:

x = 10
if (x > 5 and
    x < 20):
    # If Both conditions are satisfied
    print(x)

Or by adding extra indentation:

x = 10
if (x > 5 and
      x < 20):
    print(x)

Another type of indentation is the hanging indentation by which you can symbolize a continuation of a line of code visually:

foo = long_function_name(
      variable_one, variable_two,
      variable_three, variable_four)

You can choose any of the methods of indentation, following line breaks, in situations where the 79 character line limit forces you to add line breaks in your code, which will ultimately improve the readability.

Closing Braces in Line Continuations

Closing the braces after line breaks can be easily forgotten, so it is important to put it somewhere where it makes good sense or it can be confusing for a reader.

One way provided by PEP 8 is to put the closing braces with the first white-space character of the last line:

my_list_of_numbers = [
    1, 2, 3,
    4, 5, 6,
    7, 8, 9
    ]

Or lining up under the first character of line that initiates the multi-line construct:

my_list_of_numbers = [
    1, 2, 3,
    4, 5, 6,
    7, 8, 9
]

Remember, you can use any of the two options keeping in mind the consistency of the code.

Blank Lines

Blank lines are also called vertical whitespaces. It is a logical line consisting of spaces, tabs, formfeeds or comments that are basically ignored.

Using blank lines in top-level-functions and classes:

class my_first_class:
    pass
class my_second_class:
    pass
def top_level_function():
    return None

Adding two blank lines between the top-level-functions and classes will have a clear separation and will add more sensibility to the code.

Using blank lines in defining methods inside classes:

class my_class:
    def method_1(self):
        return None

    def method_2(self):
        return None

Here, a single vertical space is enough for a readable code.

You can also use blank spaces inside multi-step functions. It helps the reader to gather the logic of your function and understand it efficiently. A single blank line will work in such case.

An example to illustrate such:

def calculate_average(number_list):
    sum_list = 0
    for number in number_list:
        sum_list = sum_list + number
   
    average = 0
    average = sum_list / len(number_list)

   return average

Above is a function to calculate the average. There is a blank line between each step and also before the return statement.

The use of blank lines can greatly improve the readability of your code and it also allows the reader to understand the separation of the sections of code and the relation between them.

Naming Conventions

Choosing names which are sensible and can be easily understandable, while coding in Python, is very crucial. This will save time and energy of a reader. Inappropriate names might lead to difficulties when debugging.

Naming Styles

Naming variables, functions, classes or methods must be done very carefully. Here’s a list of the type, naming conventions and examples on how to use them:

TypeNaming ConventionsExamples
VariableUsing short names with CapWords.T, AnyString, My_First_Variable
FunctionUsing a lowercase word or words with underscores to improve readability.function, my_first_function
ClassUsing CapWords and do not use underscores between words.Student, MyFirstClass
MethodUsing lowercase words separated by underscores.Student_method, method
ConstantsUsing all capital letters with underscores separating wordsTOTAL, MY_CONSTANT, MAX_FLOW
ExceptionsUsing CapWords without underscores.IndexError, NameError
ModuleUsing short lower-case letters using underscores.module.py, my_first_module.py
PackageUsing short lowercase words and underscores are discouraged.package, my_first_package

Choosing names

To have readability in your code, choose names which are descriptive and give a clearer sense of what the object represents. A more real-life approach to naming is necessary for a reader to understand the code.

Consider a situation where you want to store the name of a person as a string:

>>> name = 'John William'
>>> first_name, last_name = name.split()
>>> print(first_name, last_name, sep='/ ')
John/ William

Here, you can see, we have chosen variable names like first_name and last_name which are clearer to understand and can be easily remembered. We could have used short names like x, y or z but it is not recommended by PEP 8 since it is difficult to keep track of such short names.

Consider another situation where you want to double a single argument. We can choose an abbreviation like db for the function name:

# Don't
def db(x):
    return x * 2

However, abbreviations might be difficult in situations where you want to return back to the same code after a couple of days and still be able to read and understand. In such cases, it’s better to use a concise name like double_a_variable:

# Do
def double_a_value(x):
    return x * 2

Ultimately, what matters is the readability of your code.

Comments

A comment is a piece of code written in simple English which improves the readability of code without changing the outcome of a program. You can understand the aim of the code much faster just by reading the comments instead of the actual code. It is important in analyzing codes, debugging or making a change in logic. 

Block Comments

Block comments are used while importing data from files or changing a database entry where multiples lines of code are written to focus on a single action. They help in interpreting the aim and functionality of a given block of code.

They start with a hash(#) and a single space and always indent to the same level as the code:

for i in range(0, 10):
    # Loop iterates 10 times and then prints i
    # Newline character
    print(i, '\n')

You can also use multiple paragraphs in a block comment while working on a more technical program. 

Block comments are the most suitable type of comments and you can use it anywhere you like.

Inline Comments

Inline comments are the comments which are placed on the same line as the statement. They are helpful in explaining why a certain line of code is essential.

Example of inline comments:

x = 10  # An inline comment
y = 'JK Rowling' # Author Name

Inline comments are more specific in nature and can easily be used which might lead to clutter. So, PEP 8 basically recommends using block comments for general-purpose coding.

Document Strings

Document strings or docstrings start at the first line of any function, class, file, module or method. These type of comments are enclosed between single quotations ( ''') or double quotations ( """ ).

An example of docstring:

def quadratic_formula(x, y, z, t):
    """Using the quadratic formula"""
    t_1 = (- b+(b**2-4*a*c)**(1/2)) / (2*a)
    t_2 = (- b-(b**2-4*a*c)**(1/2)) / (2*a)

    return t_1, t_2

Whitespaces in Expressions and Statements

In computing, whitespace is any character or sequence of characters which are used for spacing and have an ‘empty’ representation. It is helpful in improving the readability of expressions and statements if used properly.

Whitespace around Binary Operators

When you’re using assignment operators ( =, +=, -=,and so forth ) or comparisons ( ==, !=, >, <. >=, <= ) or booleans ( and, not, or ), it is suggested to use a single whitespace on the either side.

Example of adding whitespace when there is more than one operator in a statement:

# Don't
b = a ** 2 + 10
c = (a + b) * (a - b)

# Do
b = a**2 + 10
c = (a+b) * (a-b)

In such mathematical computations, you should add whitespace around the operators with the least priority since adding spaces around each operator might be confusing for a reader.

Example of adding whitespaces in an if statement with many conditions:

# Don't
if a < 10 and a % 5 == 0:
    print('a is smaller than 10 and is divisible by 5!')

# Do
if a<10 and a%5==0:
    print('a is smaller than 10 and is divisible by 5!')

Here, the and operator has the least priority, so whitespaces have been added around it.

Colons act as binary operators in slices:

ham[3:4]

ham[x+1 : x+2]

ham[3:4:5]

ham[x+1 : x+2 : x+3]

ham[x+1 : x+2 :]

Since colons act as a binary operator, whitespaces are added on either side of the operator with the lowest priority. Colons must have the same amount of spacing in case of an extended slice. An exception is when the slice parameter is omitted, space is also omitted.

Avoiding Whitespaces

Trailing whitespaces are whitespaces placed at the end of a line. These are the most important to avoid. 

You should avoid whitespaces in the following cases—

Inside a parentheses, brackets, or braces:

# Do
list = [1, 2, 3]

# Don't
list = [ 1, 2, 3, ]

Before a comma, a semicolon, or a colon:

x = 2
y = 3

# Do
print(x, y)

# Don't
print(x , y)

Before open parenthesis that initiates the argument list of a function call:

def multiply_by_2(a):
      return a * 2

# Do
multiply_by_2(3)

# Don't
multiply_by_2 (3)

Before an open bracket that begins an index or a slice:

# Do
ham[5]

# Don't
ham [5]

Between a trailing comma and a closing parenthesis:

# Do
spam = (1,)

# Don't
spam = (1, )

To adjust assignment operators:

# Do
variable_1 = 5
variable_2 = 6
my_long_var = 7

# Don't
variable_1    = 5
variable_2    = 6
my_long_var  = 7

Programming Recommendations

PEP 8 guidelines suggest different ways to maintain consistency among multiple implementations of Python like PyPy, Jython or Cython.

An example of comparing boolean values:

# Don't
bool_value = 5 > 4
if bool_value == True:
return '4 is smaller than 5'

# Do
if bool_value:
return '4 is smaller than 5'

Since bool can only accept values True or False, it is useless to use the equivalence operator == in these type of if executions. PEP 8 recommends the second example which will require lesser and simpler coding. 

An example to check whether a list is empty or not:

# Don't
list_value = []
if not len(list_value):
    print('LIST IS EMPTY')

# Do
list_value = []
if not list_value:
    print('LIST IS EMPTY')

Any empty list or string in Python is falsy. So you can write a code to check an empty string without checking the length of the list. The second example is more simple, so PEP encourages to write an if statement in this way.

The expression is not and not ... is are identical in functionality. But the former is more preferable due to its nature of readability:

# Do
if x is not None:
    return 'x has a value'

# Don't
if not x is None:
    return 'x has a value'

String slicing is a type of indexing syntax that extracts substrings from a string. Whenever you want to check if a string is prefixed or suffixed, PEP recommends using .startswith() and .endswith() instead of list slicing. This is because they are cleaner and have lesser chances of error:

# Do
if foo.startswith('cat'):

# Don't
if foo[:3] == 'cat':

An example using .endswith():

# Don't
if file_jpg[-3:] == 'jpg':
    print('It is a JPEG image file')

# Do
if file_jpg.endswith('jpg'):
    print('It is a JPEG image file')

Though there exists multiple ways to execute a particular action, the main agenda of the guidelines laid by PEP 8 is simplicity and readability.

When to Ignore PEP 8

You should never ignore PEP 8. If the guidelines related to PEP8 are followed, you can be confident of writing readable and professional codes. This will also make the lives of your  colleagues and other members working on the same project much easier. 

There are some exclusive instances when you may ignore a particular guideline:

  • After following the guidelines, the code becomes less readable, even for a programmer who is comfortable with reading codes that follow PEP 8.
  • If the surrounding code is inconsistent with PEP.
  • Compatible of code with older version of Python is the priority.

Checking PEP 8 Compliant Code

You can check whether your code actually complies with the rules and regulations of PEP 8 or not. Linters and Autoformatters are two classes of tools used to implement and check PEP 8 compliance.

Linters

It is a program that analyzes your code and detects program errors, syntax errors, bugs and structural problems. They also provide suggestions to correct the errors.

Some of the best linters used for Python code:

  • pycodestyle is a tool to verify the PEP 8 style conventions in your Python code.

You can run the following from the command line to install pycodestyle using pip:

pip install pycodestyle

To display the errors of a program, run pycodestyle in this manner:

pycodestyle my_code.py

my_code.py:1:11: E231 missing whitespace after '{'

my_code.py:3:19: E231 missing whitespace after ')'

my_code.py:4:31: E302 expected 2 blank lines, found 1

  • flake8 is a Python wrapper that verifies PEP 8, pyflakes, and circular complexity.

Type the command to install flake8 using pip:

pip install flake8

Run flake8 from the terminal using the command:

flake8 calc.py 

calc.py:24:3: E111 indentation is not a multiple of two

calc.py:25:3: E111 indentation is not a multiple of two

calc.py:45:9: E225 missing whitespace around operator

You can also use some other good linters like pylintpyflakes, pychecker and mypy.

Autoformatters

An autoformatter is a tool which will format your code to adapt with PEP 8 automatically.One of the most commonly used autoformatter is black.

To install black using pip, type:

pip install black

Remember, you need to have Python 3.6 or above to install black.

An example of code that doesn’t follow PEP 8:

def add(a, b): return a+b

def multiply(a, b):
      return \
        a   * b

Now run black following the filename from the terminal:

black my_code.py

reformatted my_code.py

All done! 

The reformatted code will look like:

def add(a, b):
    return a + b

def multiply(a, b):
    return a * b

Some other autoformatters include autopep8 and yapf. Their work is similar to black.

Conclusion

Since you have now learnt to write a good-quality and readable Python code using PEP 8, you’ll consider it a bliss while working in a project. Though it might be too precise in its nature, it will be useful to everyone working in a particular project by making the code more understandable and making changes and debugging easier.

Let us sum up what we’ve learnt so far:

  • What is PEP 8 and what is its importance.
  • Multiple guidelines for writing PEP 8 compliant code.
  • How to check code against PEP 8 using linters and autoformatters.

If you intend to know more about PEP 8 and its book of guidelines, you can refer to pep8.org or simply enroll for the Python certification course offered by KnowledgeHut.

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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1 comments

Ramya 16 Aug 2019

NIce article.

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Top 12 Python Packages for Machine Learning

Lovers of vintage movies would have definitely heard of the Monty Python series. The programming language that it inspired continues to remain among the most popular languages. Guess why Python has consistently topped the charts of the most popular programming languages? Because of its rich environment of libraries and tools, its easy code readability and the fact that it is so easy to pick up.  You name the domain, and you will get Python libraries available, to help you out in solving problems. Right from Artificial Intelligence, Data Science, Machine Learning, Image Processing, Speech Recognition, Computer Vision and more, Python has numerous uses. These libraries and frameworks are open source and can be easily integrated with the development environment that one has.These software frameworks, the platforms which provides necessary libraries and code components, are backbones for devloping applications. Read on to see which are the top ML frameworks and libraries in Python.1. Numpy As the name implies, this is the library which supports numerical calculations and tasks. It supports array operations and basic mathematical functions on the array and other data types of Python. The basic data type of this library is ndArray object.   Numpy has many advantagesThe base data structure is N –Dimensional array. Rich functions to handle the N-dimensional array effectively. Supports integration of C, C++ and other language code fragments. Supports many functions related to linear algebra, random numbers, transforms, statistics etc. DisadvantagesNo GPU and TPU support. Cannot automatically calculate the derivatives which is required in all ML algorithms. Numpy performance goes down when high complex calculations are required. 2. PandasThis is the most useful library for data preprocessing and preparing the data for the Machine Learning algorithms. The data from various files like CSV, Excel, Data etc. can be easily read using Pandas. The data is available in a spreadsheet like area, which makes processing easy. There are three basic data structures at the core of Pandas library: Series - One-dimensional array like object containing data and label (or index). Dataframe - Spreadsheet-like data structure containing an order collection of columns. It has both a row and column index. Panel – Collection of dataframes but rarely used data structure. AdvantagesStructured data can be read easily. Great tool for handling of data. Strong functions for manipulation and preprocessing of data. Data Exploration functions help in better understanding data. Data preprocessing capabilities help in making data ready for the application of ML algorithms. Basic Plotting functions are provided for visualization of data.  Datasets can be easily joined or merged. The functions of Pandas are optimized for large datasets. DisadvantagesGetting to know the Pandas functionalities is time consuming. The syntax is complex when multiple operations are required. Support for 3D metrics is poor. Proper documentation is not available for study. 3. MatplotlibMatplotlib is an important Python library which helps in data visualization. Understanding the data is very important for a data scientist before devising any machine learning based model. This library helps in understanding the data in a visual way. Data can be visualized using various graphical methods like line graph, bar graph, pie chart etc. This is a 2D visualization library with numerous ways of visualizing data. Image SourceAdvantagesSimple and easy to learn for beginners. Integrated with Pandas for visualization of data in effective way. Various plots are provided for better understanding of data like Bar Chart, Stacked Bar chart, Pie chart, Scatter Plot etc. Forms a base for many advanced plotting libraries. Supports storing of the various graphs as images so that they can be integrated with other applications. Can plot timeseries data (with date) very easily. DisadvantagesComplex Syntax for plotting simple graphs. The code becomes lengthy and complex for visualizations. Support for plotting of categorial data is not provided. It is a 2D visualization library. When multiple fields are required to be plotted and visualized effectively, matplotlib code can become lengthy. Managing multiple figures is difficult. 4. Seaborn Visualizations are made simpler and more advanced with the help of Seaborn library. The base for Seaborn is Matplotlib. It is a boon for programmers as statistical visualizations are simplified. Image sourceAdvantagesBest high-level interface for drawing statistical graphics. Provides support for plotting of categorial data effectively. The library provides default themes and many visualization patterns. Multiple figures are automatically created. The syntax is very simple and compact. There are many methods to integrate with Pandas dataframe, making this library most useful for visualization. DisadvantagesMemory issues due to creation of multiple figures. Less customizable and flexible as compared to Matplotlib. Scalability issues. 5. Scipy   Scipy is a Scientific Python library based on Numpy. It has functions which are best suitable for Mathematics, Science and Engineering. Many libraries are provided for Image and Signal Processing, Fourier Transform, Linear Algebra, Integration and Optimization. The functions are useful for ML algorithms and programs. AdvantagesThe base library is Numpy. Many ML related functions are provided like Linear Algebra, Optimization, Compressed Sparce Data Structure etc. Useful Linear Algebra functions are available which are required for implementation of ML related algorithms. The functions can be applied with Pandas Dataframe directly. DisadvantagesComplex functions are available and domain knowledge is needed to understand and implement these functions. There are performance issues when data size increases. Many other effective alternative libraries are available with the needed functionality. 6. Scikit-Learn Scikit-Learn is a useful open access library for use to Python developers. It is an extensive and popular library with many Machine Learning Supervised and Unsupervised algorithms implemented. These algorithms can be fine-tuned with the help of hyperparameters. This library contains many useful functions for preprocessing of data, useful metrics to measure performance of algorithms and optimization techniques.  AdvantagesIt is a general Machine Learning library built on top of Numpy, Pandas and Matplotlib. Simple to understand and use even for novice programmers. Useful Machine Learning Algorithms, both Supervised and Unsupervised, are implemented. Popular library for doing Machine Learning related tasks. Rich in Data Preprocessing and Data Sampling functions and techniques. Plethora of evaluation measures implemented to track the performance of algorithms. Very effective for quick coding and building Machine Learning Models. DisadvantagesScikit learn, as is based on Numpy, requires additional support to run on GTP and TPU Performance is an issue with size of data. Best suitable for basic Machine Learning applications. This library may be useful if one wants to write easy code, but it’s not the best choice for more detailed learning. 7. NLTK Natural Language processing is a great field of study for developers who like to research and challenge themselves. This library provides a base for Natural Language processing by providing simple functionalities to work with and understand languages.AdvantagesVery simple to use for processing natural language data. Many basic functionalities like tokenizing the words, removal of stop words, conversion to word vectors etc. are provided which forms the basis to start with natural language processing models. It is an amazing library to play with natural language using Python. It has more than 50 trained models and lexical resources like wordnet available for use. Rich discussion forums and many examples are available to discuss how to use this library effectively. DisadvantagesIt is based on string processing, which itself has many limitations. Slower as compared to other Natural Language processing libraries like Spacy.8. Keras Keras is a library written in Python for Neural Network programming. It offers a very simple interface to code the neural network and related algorithms. It is an incredibly popular library for Deep Learning algorithms, models and applications and can also be combined with various deep learning frameworks. It provides support for GPU and TPU computation of algorithms. The API provided is simple, same as Scikit-learn. Keras is totally based on Models and Graphs. A model has Input, output and intermediate layers to perform the various tasks as per requirement. Effective functionalities and models provided to code deep learning algorithms like Neural Network, Recurrent Neural Network, Long Short-Term memory, Autoencoders etc. Allows to create products easily supporting multiple backends Supports multi-platform use. Can be used with TensorFlow, can be used in browser using web based keras and provides native ML support for iPhone app development. 9. TensorFlow TensorFlow is the talk of the town because of its capabilities suitable for Machine Learning and Deep Learning models. It is one of the best, and most popular frameworks, adopted by companies around the world for Machine Learning and Deep Learning. Its support for Web as well as Mobile application coupled with Deep Learning models has made it popular among engineers and researchers. Many giants like IBM, Dropbox, Nvidia etc. use TensorFlow for creating and deploying Machine Learning Models. This library has many applications like image recognition, video analysis, speech recognition, Natural Language Processing, Recommendation System etc. TensorFlow lite and TensorFlow JS has made it more popular for web applications and Mobile Applications. Advantages Developed by Google, it is one of the best deep learning frameworks. Simple Machine Learning tasks are also supported in TensorFlow. Supports many famous libraries like scikit learn, Keras etc. which are part of TensorFlow. The basic unit is Tensor which is an n-dimensional array. The basic derivatives are inherently computed which helps in developing many Machine learning Models easily. The models developed are supported on CPT, TPU and GPU. Tensorboard is the effective tool for data visualization. Many other supported tools are available to facilitate Web Development, App Development and IoT Applications using Machine Learning. Disadvantages Understanding Tensor and computational graphs is tedious. Computational graphs make the code complex and sometimes face performance problems. 10. Pytorch A popular Python framework, Pytorch supports machine learning and deep learning algorithms and is a scientific computing framework. This is a framework which is widely used by Twitter, Google and Facebook. The library supports complex Tensor computations and is used to construct deep neural networks. AdvantagesThe power of Pytorch lies in construction of Deep Neural Networks. Rich functions and utilities are provided to construct and use Neural Networks. Powerful when it comes to creation of production ready models. It supports GPU operations with rich math-based library functions. Unlike Numpy, it provides the functions which calculates gradient of the function, useful for the construction of the neural network. Provides support for Gradient based optimization which helps in scaling up the models easily to large data. Disadvantages It is a complex framework, so learning is difficult. Documentation support for learning is not readily available. Scalability may be an issue as compared to TensorFlow. 11. Theano Theano is a library for evaluating and optimizing the mathematical computations. It is based on NumPy but provides support for both the GPU and CPU. AdvantagesIt is a fast computation library in Python. Uses native libraries like BIAS to turn the code in faster computation. Best suited to handle computations in Deep Learning algorithms. Industry standard for Deep Learning research and development. Disadvantages It is not very popular among researchers as it is one of the older frameworks. It is not as easy to use as TensorFlow.12. CNTK CNTK is Microsoft’s Cognitive Toolkit for the development of Deep Learning based models. It is a commercial distributed deep learning tool. AdvantagesIt is a distributed open-source deep learning framework. Popular models like Deep Neural Network, Convolutional Neural Network models can be combined easily to form new models. Provides interface with C, C++ and Java to include Machine Learning models. Can be used to build reinforcement learning models as wide functions are available. Can be used to develop GAN (Generative Adversarial Networks). Provides various ways to measure the performance of the models built. High accuracy parallel computation on Multiple GPU is provided. Disadvantages Proper documentation is not available. There is inadequate community support. ConclusionPython, being one of the most popular languages for the development of Machine Learning models, has a plethora of tools and frameworks available for use. The choice of tool depends on the developer’s experience as well as the type of application to be developed. Every tool has some strong points and some weaknesses, so one has to carefully choose the tool or framework for the development of Machine Learning based applications. The documentation and support available are also important criteria to be kept in mind while choosing the most appropriate tool. 
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Top 12 Python Packages for Machine Learning

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Apart from that, Oracle also provides exam vouchers for this certification course.Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to settle as a Java developer or start his/her career as a Java programmer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fee for Certification: $ 245Application fee for certification:OCAJP8: $ 245OCAJP11: $ 249Exam fee for certification:OCAJP8: $ 245OCAJP11: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.3. Certified Associate in Python Programming (PCAP)Python is an interpreted, general-purpose, and high-level programming language developed by Guido Van Rossum. Python released in 1991 and within 5 to 6 years, this programming language become the most popular and widely used programming language in various disciplines. Today, companies use Python for GUI and CLI-based software development, web development (server-side), data science, machine learning, AI, robotics, drone systems, developing cyber-security tools, mathematics, system scripting, etc. PCAP is a professional Python certification credential that measures your competency in using the Python language to create code and your fundamental understanding of object-oriented programming.It comprises of topics likeBasic concepts of PythonOperators & data typesControl and EvaluationsModules and PackagesData AggregatesException HandlingStringsFunctions and ModulesObject-Oriented ProgrammingList Comprehensions, Lambdas, Closures, and I/O OperationsClasses, Objects, and ExceptionsDemand and Benefits: Having a Python certification verifies that the programmer or the aspirant has all the necessary and essential skills needed to become an expert Python developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The average entry-level salary of a Python developer starts at around $100k per annum. With a few years of experience, the average salary hikes to $ 105k annually.Top companies and organizations hiring certified Python programmers are Bank of America, Atlassian, Google, Adobe, Apple, Cisco Systems, Intel, Lyft, IBM, etc.Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for beginners in Python programming. It has hands-on learning with 24 hours of instructor-led online lectures. Apart from that, the course has 100 hours of MCQs and three live projects.Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, or computer science aspirant - who wants to pursue a career as a Python developer or  Python programmer can opt for this certification training. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 295Exam fee for certification: $ 295Retake fee for certification: If a candidate fails the exam, he/she has to wait for 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam.4. MongoDB Certified Developer Associate ExamMongoDB is a NoSQL, document-based high-volume heterogeneous database system. Instead of having tables with rows and columns, MongoDB uses a collection of documents. It is a database development system that provides scalability and flexibility as per query requirements. Its document models are easy to implement for developers and can meet complex demands at scale.MongoDB created this MongoDB Certified Developer Associate Exam for individuals who require to verify their knowledge on fundamentals of designing and building applications using MongoDB. They recommend this certification for those who want to become software engineers and have a solid understanding of core MongoDB along with professional experience.It comprises of topics likeMongoDB BasicsCRUDIndexing and PerformanceThe MongoDB Aggregation FrameworkBasic Cluster AdministrationAggregation & ReplicationShardingMongoDB Performance  MongoDB for Python DevelopersMongoDB for Java Developers or MongoDB for JavaScript DevelopersData ModelingDemand and Benefits: Having a MongoDB Certified Developer Associate Exam certification verifies that the programmer or the aspirant has all the necessary and essential skills to become a NoSQL database expert. The MongoDB certification is inexpensive and in demand. The average salary for a software developer with MongoDB skills starts from $ 8200 per annum.Top companies and organizations hiring certified MongoDB developers are Accenture, Collabera, Leoforce LLC., Adobe, Trigent Software, Lyft, etc.Where to take Training for Certification: KnowledgeHut has a comprehensive course structure for those who want to learn MongoDB & Mongodb Administrator. It has 24+ hours of instructor-led online lectures and 80+ hours of hands-on with cloud labs. This self-paced course also includes capstone projects to give participants a feel of real world working.  Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to embark on a career as a MongoDB developer or start his/her career as a NoSQL database expert or do better in their current role as a MongoDB developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 150Exam fee for certification: $ 150Retake fee for certification: MongoDB University is no longer allowing a free retake with the exam fee. The candidate has to pay an additional $10 to reschedule or retake the exam.5. R Programming CertificationIt is a part of the data science specialization from Johns Hopkins University under Coursera. This course teaches R programming for efficient data analysis. It covers different R programming concepts like building blocks of R, datatypes, reading data into R from external files, accessing packages, writing functions, debugging techniques, profiling R code, and performing analysis.It comprises of topics like:Basic building blocks in RData types in RControl StructuresScoping Rules - OptimizationCoding StandardsDates and TimesFunctionsLoopingDebugging toolsSimulating data in RR ProfilerDemand and Benefits: Having an R Programming certification verifies that the programmer or the aspirant has all the necessary and essential skills require to get a job role as data analyst. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified R programmer with this certification is ₹ 508,224 per annum.Top companies and industries hiring certified R programmers are Technovatrix, CGI Group Inc., Amazon, Sparx IT Solutions, Accenture, Uber, etc.Where to take Training for Certification: KnowledgeHut has a fascinating training course for those who wants to become a R programmer. It has 22+ hours of instructor-led live training and three self-paced live projects.Who should take the Training (roles) for Certification: Any data analyst, graduate, post graduate student, experienced data analyst or computer science aspirant - who wants to settle as a R programmer or data analyst can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: FreeFee for certification: $ 60 (Coursera Plus Monthly)Retake fee for certification: Free6. Oracle MySQL Database Administration Training and Certification (CMDBA)It is another course offered by Oracle for SQL developers. Oracle University designed this course for database administrators who want to validate their skills with developing performance, blending business processes, and accomplishing data processing work. Structured Query Language (SQL) is one of the top database management query languages that allows us to access and manipulate databases. If you want to verify your database skills during a job interview or impress your peers at your workplace then this certification is worth getting. This certification path includes Professional, Specialist, and Developer levels. The candidate should pass the MySQL Database Administrator Certified Professional Exam Part 1 & Part 2 to earn the certification.It comprises of topics likeInstalling MySQLMySQL ArchitectureConfiguring MySQLUser ManagementMySQL SecurityMaintaining a Stable SystemOptimizing Query PerformanceBackup StrategiesConfiguring a Replication TopologyDemand and Benefits: Having an CMDBA certification verifies that the programmer or the aspirant has all the necessary and essential skills required to get a job role as SQL developer. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified MySQL DBA or backend developer with this certification is $ 66,470 per annum.Top companies and industries hiring Certified MySQL database administrators are Fiserv, IBM, HCL, Adobe, Microsoft, Apple, Accenture, Collabera, and more.Where to take Training for Certification: KnowledgeHut has a cutting-edge curriculum for those who want to become  MySQL database administrators. It has 16+ hours of instructor-led online lectures and 80+ hours of hands-on lab. Apart from that, this self-paced course has Capstone projects.Who should take the Training (roles) for Certification: Any developer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to pursue a career as a DBA or backend developer or start his/her career in database management or backend software development can opt for this certification course. There is no other prerequisite to appear for this exam or course.Course fees for Certification: $ 255Exam fee for certification: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days after the initial attempt.7. CCA Spark and Hadoop DeveloperWith the exponential growth in data, IT firms and organizations have to manage this tremendous amount of data generated. So, many companies are actively looking for Big data and Spark developers who can optimize performance. Big Data is the term used to describe enormous volumes of data. Apache Spark supports data management as it is an open-source centralized analytics engine that handles large-scale data processing.It requires prerequisite knowledge of Scala and Python. This certification also verifies and showcases your skills through Spark and Hadoop projects. Passing this certification course gives you a logo and a license to authenticate your CCA status.It comprises of topics likeLoad data from HDFS for use in Spark applicationsWrite the results back into HDFS using SparkRead and write files in a variety of file formatsPerform standard extract, transform, load (ETL) processes on data using the Spark APIUse metastore tables as an input source or an output sink for Spark applicationsUnderstand the fundamentals of querying datasets in SparkFilter data using SparkWrite queries that calculate aggregate statisticsJoin disparate datasets using SparkProduce ranked or sorted dataSupply command-line options to change your application configuration, such as increasing available memoryDemand and Benefits: Passing the CCA Spark and Hadoop Developer Exam (CCA175) by Cloudera verifies that you have all the essential skills required to get a job as a Hadoop developer and handle Big data projects. The average salary of a certified CCA Spark and Hadoop Developer with this certification is $ 74,200 per annum.Top companies and industries hiring Certified Spark and Hadoop Developers are Primus Global, IBM, Collabera, CorroHealth, Genpact, Xerox, Accenture, and more.Where to take Training for Certification: KnowledgeHut has extensive courses for those who want to become Big Data experts and want to work as Hadoop developers. It has different courses on Big Data Analytics, Apache Storm, Hadoop Administration, Apache Spark & Scala, Big Data with Hadoop, and more.Who should take the Training (roles) for Certification: Any Big Data developer, graduate & post graduate students, Hadoop developer or computer science aspirant - who wants to make a career in Big data development or start his/her career as a Big Data or Hadoop project developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification: $ 295Application fee for certification: $ 295Exam fee for certification: $ 295Retake fee for certification: Within 30 to 60 minutes of exam completion, Cloudera will send a scorecard mail with a pass or fail status. If the candidate fails the exam, then they have to wait for 30 days for another try.  Cloudera gives additional discounts on retakes.ConclusionWhether you are starting your career as a coder or are an experienced programmer looking to grow in the industry, having a certification and proper knowledge of any popular programming language is one of the most proven ways to elevate your programming career.  We trust that this article will help you to understand your area of interest. Choose the programming language you wish to make a career in, wisely. This would also depend on your pre-existing knowledge. If you aren't sure which resource will be more informative for doing your certification as per your area of interest, KnowledgeHut (https://www.knowledgehut.com/) has all the support and expert trainers who can guide you, from start to finish—that is in clearing the exam and helping you gain sound knowledge of your preferred subject.Receiving a programming certification is an added bonus which will make you stand out from the rest. Proper training from an institute such as KnowledgeHut will help you gain skills that are relevant and in demand in the industry.
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Top-Paying Programming Certifications for 2021

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Top IT Certifications for Java Developers in 2021

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

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