{"id":77873,"date":"2020-04-04T17:25:33","date_gmt":"2020-04-04T11:55:33","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=77873"},"modified":"2020-04-04T17:25:33","modified_gmt":"2020-04-04T11:55:33","slug":"python-terminologies","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/python-terminologies\/","title":{"rendered":"Python Terminologies – Important terms you must know as a Python Developer"},"content":{"rendered":"

In this article, we will make you familiar with the important concept in Python that is, Python terminologies<\/strong>. In future, you will come across all these terms in various books<\/strong> and tutorials<\/strong> while learning Python.<\/p>\n

So without wasting any time let\u2019s begin with our tutorial on Python Terminologies!<\/p>\n

Python Terminologies<\/h3>\n

\"\"<\/a><\/p>\n

1. >>><\/h4>\n

When you open the Python shell<\/strong>, you will find this as the default Python prompt<\/strong> of the shell<\/strong>. This is where you write small snippets<\/strong> of code<\/strong> and press enter<\/strong> to run<\/strong> it.<\/p>\n

2. 2to3<\/h4>\n

As Python 2<\/strong> is soon going to be obsolete<\/strong>, coders are now switching<\/strong> from Python 2 to 3<\/strong>.<\/p>\n

To make the transition<\/strong> easier<\/strong>, we have a tool<\/strong> that converts<\/strong> any code<\/strong> written in Python 2.x to Python 3.x<\/strong>. It is available in the standard library<\/strong>.<\/p>\n

3. annotation<\/h4>\n

Annotations<\/strong> specify the expected type<\/strong> for a variable<\/strong>, a class attribute<\/strong>, or a function parameter<\/strong> or return value<\/strong> through a \u2018type hint\u2019<\/strong>.<\/p>\n

4. argparse<\/h4>\n

It is a command-line parsing module<\/strong> in Python\u2019s standard library that parses command-line<\/strong> options, arguments<\/strong> and subcommands<\/strong>.<\/p>\n

5. argument<\/h4>\n

It is the value we pass<\/strong> to a function<\/strong> during the function call<\/strong>. There are two<\/strong> basic types of arguments:<\/p>\n

    \n
  • Keyword Argument:<\/strong> an argument preceded<\/strong> by an identifier<\/strong>.<\/li>\n<\/ul>\n

    Example,\u00a0<\/strong><\/p>\n

    power(base= 4, exponent= 2)<\/pre>\n
      \n
    • Positional Argument:<\/strong> an argument which has no identifier<\/strong> associated with it.<\/li>\n<\/ul>\n
      Example<\/strong>,<\/p>\n
      power(4,2)<\/pre>\n
      6. assignment<\/h4>\n
      The assignment is the process of assigning value<\/strong> to a variable<\/strong>. We depict the assignment operator by the equal sign (=)<\/strong>.<\/p>\n
      The variable on the left side<\/strong> of the equal sign<\/strong> is assigned the value on the right side<\/strong>.<\/p>\n
      7. asynchronous context manager<\/h4>\n
      An asynchronous context manager is an object<\/strong> that controls<\/strong> the environment<\/strong> by defining __aenter__()<\/strong> and __aexit__()<\/strong> methods. It allows you to allocate<\/strong> and release<\/strong> the resources<\/strong> as you need.<\/p>\n
      8. asynchronous generator<\/h4>\n
      It is a function that returns<\/strong> an asynchronous generator iterator<\/strong>. It looks similar to our usual function except that we define it using \u2018async def\u2019<\/strong> instead of \u2018def\u2019<\/strong>, and it contains \u2018yield\u2019 expressions<\/strong>.<\/p>\n
      9. Asynchronous generator iterator<\/h4>\n
      An asynchronous generator function returns<\/strong> an object<\/strong>. This object<\/strong> is an asynchronous generator iterator<\/strong>.<\/p>\n
      We can this object using the __anext__()<\/strong> method. It returns<\/strong> an object<\/strong> which will execute<\/strong> the body<\/strong> of the asynchronous generator function<\/strong> till it encounters<\/strong> the next \u2018yield\u2019 expression<\/strong>.<\/p>\n
      10. asynchronous iterable<\/h4>\n
      It is an object that returns an asynchronous iterator from its __aiter__()<\/strong> method.<\/p>\n
      11. asynchronous iterator<\/h4>\n
      This is an object which implements the __aiter__()<\/strong> and __anext__()<\/strong> methods.<\/p>\n
      The __anext__()<\/strong> method returns<\/strong> an awaitable<\/strong> object<\/strong>. We can iterate over this object using the \u2018async for\u2019<\/strong> statement.<\/p>\n
      12. attribute<\/h4>\n
      An attribute is a value associated<\/strong> with an object<\/strong>. We reference it using the \u201cdot operator\u201d<\/strong>.<\/p>\n
      For example, if an object obj<\/strong> has an attribute attr<\/strong> we reference it as obj.attr<\/strong>.<\/p>\n
      13. assert<\/h4>\n
      We use assert statements while debugging<\/strong> a test condition<\/strong>. If the condition is true<\/strong>, it does nothing<\/strong>.<\/p>\n
      But if the condition is false<\/strong>, it raises an AssertionError<\/strong> exception.<\/p>\n
      14. BDFL<\/h4>\n
      The creator<\/strong> of Python<\/strong>, Guido Van Rossum<\/strong>, was titled the \u201cBenevolent Dictator for Life\u201d!<\/strong><\/p>\n
      Told you Python was interesting<\/strong> in ways you couldn\u2019t have imagined!!<\/p>\n
      15. block<\/h4>\n
      It is a group of statements<\/strong> executing<\/strong> as a single unit<\/strong>.<\/p>\n
      16. binary file<\/h4>\n
      A file object<\/strong> that stores<\/strong> information<\/strong> in the form of bytes<\/strong>, i.e., 0s<\/strong> and 1s<\/strong>. A binary file object<\/strong> is able to read<\/strong> and write<\/strong> bytes-like objects<\/strong>.<\/p>\n
      17. bytecode<\/h4>\n
      Before translating the Python source code<\/strong> to machine code<\/strong>, we first compile<\/strong> it into a low-level intermediate code<\/strong>. This intermediate<\/strong> code<\/strong> is the bytecode<\/strong>.<\/p>\n
      A file with a .pyc<\/strong> extension<\/strong> contains this bytecode<\/strong>.<\/p>\n
      18. break<\/h4>\n
      A break statement \u2018breaks<\/strong>\u2019 the usual control flow<\/strong> of execution<\/strong>. We use break to exit<\/strong> out of a loop<\/strong>.<\/p>\n
      19. class<\/h4>\n
      This is a blueprint<\/strong> for creating user-defined objects<\/strong> of similar type<\/strong>. Class definitions group <\/strong>together data<\/strong> and method<\/strong> definitions operating<\/strong> on that data<\/strong>.<\/p>\n
      20. class variable<\/h4>\n
      This is the variable whose definition is present inside a class<\/strong> and belongs only<\/strong> to that class<\/strong>. The scope<\/strong> of such a variable<\/strong> is confined at the class level<\/strong> and cannot<\/strong> be modified<\/strong> or accessed<\/strong> outside of it.<\/p>\n
      21. coercion<\/h4>\n
      While performing an operation<\/strong> that involves two arguments<\/strong> of the same type, Python might have to implicitly convert<\/strong> an instance of one type<\/strong> to another<\/strong>. This process of converting<\/strong> a value of one type<\/strong> to another<\/strong> is known as coercion<\/strong>.<\/p>\n
      Example, to perform 2 \/ 3.0<\/strong>, the interpreter will have to convert int 2<\/strong> into float 2.0<\/strong>, resulting in:<\/p>\n
      >>> 2\/3.0
      \n0.6666666666666666
      \n>>><\/div>\n
      22. compiler<\/h4>\n
      A compiler translates<\/strong> a high-level language program<\/strong> to a low-level language<\/strong>.<\/p>\n
      23. context manager<\/h4>\n
      An object<\/strong> that controls<\/strong> and manages<\/strong> the resources<\/strong> and the environment as seen in a \u2018with\u2019 statement<\/strong>.<\/p>\n
      24. context variable<\/h4>\n
      Depending on different contexts<\/strong>, a variable<\/strong> can have different values<\/strong>. Such variables are known as context variables<\/strong>.<\/p>\n
      25. contiguous<\/h4>\n
      A buffer<\/strong> is considered contiguous if all the items in the buffer are laid out next to each other<\/strong> in the memory<\/strong>. A buffer is contiguous exactly if it is either C-contiguous<\/strong> or Fortran contiguous<\/strong>.<\/p>\n
      26. coroutine<\/h4>\n
      A more generalized form of subroutines<\/strong> is termed as a coroutine<\/strong>. Coroutines are implemented using the \u2018async def\u2019<\/strong> statement.<\/p>\n
      27. continue<\/h4>\n
      The continue statement alters<\/strong> the usual flow<\/strong> of execution<\/strong> of statements<\/strong>. It is used within a loop<\/strong>.<\/p>\n
      When the interpreter encounters a continue statement<\/strong> it skips<\/strong> the remaining statements<\/strong> in the current iteration<\/strong> and jumps<\/strong> to the next iteration<\/strong> if the condition<\/strong> of the loop<\/strong> evaluates to true<\/strong>.<\/p>\n
      28. conditional statement<\/h4>\n
      Conditional statements determine which block of code<\/strong> should be executed<\/strong> depending on the boolean value<\/strong> of a conditional expression<\/strong>.<\/p>\n
      29. CPython<\/h4>\n
      CPython is the standard implementation<\/strong> of the Python Programming language.<\/p>\n
      Coders<\/strong> and authors<\/strong> explicitly<\/strong> use this term while distinguishing<\/strong> this implementation of Python<\/strong> from others such as Jython<\/strong> or IronPython<\/strong>.<\/p>\n
      30. debugging<\/h4>\n
      Debugging refers to the process of finding<\/strong> and removing bugs(errors)<\/strong> in a code<\/strong>.<\/p>\n
      31. decorator<\/h4>\n
      We use decorators to add new functionality<\/strong> to an existing object<\/strong> or function<\/strong> without modifying<\/strong> its structure<\/strong>.<\/p>\n
      Examples include, classmethod()<\/strong> and staticmethod()<\/strong>.<\/p>\n
      32. descriptor<\/h4>\n
      Any object defining the methods __get__()<\/strong>, __set__()<\/strong>, or __delete__()<\/strong> is termed as a descriptor<\/strong>.<\/p>\n
      Descriptors form a basis for many features, like functions<\/strong>, methods<\/strong>, properties<\/strong>, class methods<\/strong>, static methods<\/strong>, making them significant in Python.<\/p>\n
      33. dictionary<\/h4>\n
      It is an unordered collection<\/strong> of key-value pairs<\/strong>. It is a mapping<\/strong> of arbitrary keys<\/strong> to values<\/strong>.<\/p>\n
      34. dictionary views<\/h4>\n
      The methods dict.keys()<\/strong>, dict.values()<\/strong>, and dict.items()<\/strong>, return some objects<\/strong>. These objects<\/strong> are known as dictionary views<\/strong>. These methods access<\/strong> the keys<\/strong> and values<\/strong> of the dictionary<\/strong>.<\/p>\n
      35. docstring<\/h4>\n
      It is a string<\/strong> that contains a brief description<\/strong> of what a function<\/strong>, class<\/strong> or module<\/strong> does. It is often included as the first line<\/strong> of a function<\/strong>, class<\/strong> or module<\/strong>.<\/p>\n
      36. duck-typing<\/h4>\n
      Duck typing is a programming style<\/strong> used by Python.<\/p>\n
      \u201cIf it looks like a duck and quacks like a duck, it must be a duck.\u201d <\/em><\/strong><\/p>\n
      Since Python is \u2018dynamically-typed\u2019<\/strong>, it uses duck typing to determine the type of an object<\/strong>.<\/p>\n
      Duck typing believes that while determining<\/strong> if the object<\/strong> is suitable<\/strong> for a given purpose<\/strong>, an object\u2019s type<\/strong> is less important<\/strong> than the method<\/strong> it defines<\/strong>.<\/p>\n
      37. exception<\/h4>\n
      An exception is an unwanted event<\/strong> that can disrupt<\/strong> the normal flow of execution<\/strong> of our program and may result<\/strong> in a halt<\/strong> or complete termination<\/strong> of our program<\/strong>.<\/p>\n
      38. expression<\/h4>\n
      An expression is a piece of syntactical code<\/strong> that results<\/strong> in some value<\/strong>. It is made up of expression elements like literals<\/strong>, names<\/strong>, operators<\/strong> or function calls<\/strong> which all return some value<\/strong>.<\/p>\n
      39. extension module<\/h4>\n
      It is a module<\/strong> written in another language like C<\/strong> or C++<\/strong>, using Python\u2019s C Application Programming Interface<\/strong>.<\/p>\n
      40. f-string<\/h4>\n
      The f-strings, stands for formatted string literals<\/strong> are those strings which are prefixed<\/strong> with \u2018f\u2019<\/strong> or \u2018F\u2019<\/strong>. A formatted string<\/strong> contains normal strings together with \u201cargument specifiers\u201d<\/strong> or \u201cplace-holders\u201d<\/strong>.<\/p>\n
      41. file object<\/h4>\n
      File objects<\/strong> are used to perform operations<\/strong> on a file<\/strong>. These operations include accessing<\/strong> and manipulating<\/strong> files<\/strong> by reading<\/strong> and writing<\/strong> to it.<\/p>\n
      42. finder<\/h4>\n
      When we import a module<\/strong> into our Python code<\/strong>, the interpreter is responsible for loading<\/strong> it into our program<\/strong>. A finder<\/strong> is an object<\/strong> which finds<\/strong> the loader<\/strong> for the imported module<\/strong>.<\/p>\n
      43. floor division<\/h4>\n
      Floor division is a mathematical division operation<\/strong> that rounds<\/strong> down the quotient<\/strong> to the nearest integer<\/strong>. Floor division operator is denoted by \/\/<\/strong>.<\/p>\n
      Example,<\/p>\n
      >> 3\/\/2
      \n1<\/div>\n
      44. function<\/h4>\n
      A block of statements<\/strong> which performs some operation<\/strong> and returns some value<\/strong> to the caller<\/strong>. A function<\/strong> can be passed zero<\/strong> or more arguments<\/strong>.<\/p>\n
      45. function annotation<\/h4>\n
      Function annotations indicate the type<\/strong> of function parameters<\/strong> and the expected type<\/strong> of value<\/strong> the function<\/strong> should return<\/strong>.<\/p>\n
      Example,<\/strong><\/p>\n
      def add(a: int, b: int) -> int:\n   return a + b<\/pre>\n
      In this function, the two parameters<\/strong> should be integer type<\/strong> and the function<\/strong> should return<\/strong> a value<\/strong> of type integer<\/strong> as well.<\/p>\n
      46. garbage collection<\/h4>\n
      Garbage collection refers to the process of freeing (collecting) memory<\/strong> which is not in use<\/strong> anymore<\/strong> so that it can be used later<\/strong>. The control<\/strong> of garbage collection<\/strong> lies within the gc module<\/strong>.<\/p>\n
      47. generator<\/h4>\n
      A generator is a function that returns<\/strong> a generator iterator<\/strong>. It looks similar to the normal function definition except that it contains \u2018yield\u2019 expressions<\/strong>.<\/p>\n
      48. generator iterator<\/h4>\n
      An object created<\/strong> and returned<\/strong> by a generator function<\/strong>. Each yield temporarily<\/strong> suspends <\/strong>the execution<\/strong> and the generator iterator<\/strong> picks up<\/strong> where it left off<\/strong> in the next execution<\/strong> state.<\/p>\n
      49. generator expression<\/h4>\n
      An expression<\/strong> that is followed by a \u2018for\u2019<\/strong> clause<\/strong> and returns an iterator<\/strong>.<\/p>\n
      Example,<\/strong><\/p>\n
      >> sum(i for i in range(10))<\/pre>\n
      This expression returns <\/strong>the sum<\/strong> of all<\/strong> the numbers<\/strong> from 0 to 9<\/strong>.<\/p>\n
      50. Generic function<\/h4>\n
      It is the function<\/strong> which contains multiple implementations<\/strong> of the same operation<\/strong> for different data types<\/strong>. The implementation<\/strong> to be used<\/strong> during a call<\/strong> is determined at runtime<\/strong> using the dispatch algorithm<\/strong>.<\/p>\n
      51. High level language<\/h4>\n
      A programming language which is easy<\/strong> for humans<\/strong> and difficult<\/strong> for machines<\/strong> to read<\/strong> and understand<\/strong>.<\/p>\n
      52. hashable<\/h4>\n
      An object<\/strong> is hashable<\/strong> if the hash value<\/strong> generated for it (using a __hash__() method)<\/strong> never changes<\/strong> during its lifetime.<\/p>\n
      53. IDLE<\/h4>\n
      IDLE<\/strong> is short for Integrated Development Environment<\/strong> for Python. It is the editor<\/strong> and interpreter<\/strong> used for building<\/strong> and executing Python programs<\/strong>.<\/p>\n
      54. immutable<\/h4>\n
      An object whose value can\u2019t<\/strong> be changed<\/strong> during execution<\/strong> is known as an immutable object<\/strong>.<\/p>\n
      Example, numbers<\/strong>, strings<\/strong>, tuples<\/strong>.<\/p>\n
      55. import path<\/h4>\n
      It is a list of locations<\/strong> where the path based finder searches<\/strong> for modules to import<\/strong>. This list usually comes from sys.path<\/strong>.<\/p>\n
      56. importing<\/h4>\n
      Importing means loading the code<\/strong> of one module<\/strong> to another module<\/strong> so that it can be reused<\/strong>.<\/p>\n
      57. Importer<\/h4>\n
      An object<\/strong> which is responsible<\/strong> for finding<\/strong> and loading<\/strong> a module<\/strong> that is being imported<\/strong>.<\/p>\n
      58. Indentation<\/h4>\n
      Python uses whitespaces<\/strong> to mark<\/strong> the beginning<\/strong> and end<\/strong> of a code block<\/strong>.<\/p>\n
      59. Interactive mode<\/h4>\n
      Python\u2019s interpreter<\/strong> is interactive<\/strong> in the sense that you can run small snippets<\/strong> of code<\/strong> in the Python shell<\/strong>. The interpreter will immediately run<\/strong> them for you and show the result<\/strong> for it.<\/p>\n
      60. interpreted<\/h4>\n
      Python uses an interpreter<\/strong> for converting<\/strong> its source code<\/strong> into machine code<\/strong> and executing<\/strong> it. That\u2019s why Python is known as an interpreted language<\/strong>.<\/p>\n
      61. Interpreter shutdown<\/h4>\n
      You can ask the interpreter<\/strong> to shut down <\/strong>upon which the interpreter will enter a phase<\/strong> where it will release<\/strong> all the allocated resources<\/strong>. It will also make calls<\/strong> to the garbage collector<\/strong>.<\/p>\n
      62. iterable<\/h4>\n
      An object<\/strong> which can be iterated over<\/strong> or which can return<\/strong> its members one at a time<\/strong> is known as an iterable<\/strong>.<\/p>\n
      Examples<\/strong> include lists<\/strong>, sets<\/strong>, tuples<\/strong>, dictionaries<\/strong>, strings<\/strong> etc.<\/p>\n
      63. iterator<\/h4>\n
      This is an object that represents a stream of data<\/strong>. The successive items<\/strong> in this stream can be returned<\/strong> using repeated calls<\/strong> to __next__()<\/strong> method.<\/p>\n
      Summary<\/h3>\n
      Here we come to the end of our article on Python Terminologies<\/strong>. In this article, we explored few numbers<\/strong> of terminologies<\/strong> in Python<\/strong>.<\/p>\n
      This was just the first part of all the important terminologies<\/strong> we have in Python.<\/p>\n","protected":false},"excerpt":{"rendered":"
      In this article, we will make you familiar with the important concept in Python that is, Python terminologies. In future, you will come across all these terms in various books and tutorials while learning...<\/p>\n","protected":false},"author":1,"featured_media":77998,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1053],"tags":[2242,2243,2244,2245,2246],"class_list":["post-77873","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-python","tag-python-terminologies","tag-python-terminology","tag-python-terms","tag-terminologies-in-python"],"yoast_head":"\nPython Terminologies - Important terms you must know as a Python Developer - TechVidvan<\/title>\n<meta name=\"description\" content=\"Make yourself aware with some terminologies in Python in detail. 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