{"id":10467,"date":"2018-03-12T06:38:13","date_gmt":"2018-03-12T01:08:13","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=10467"},"modified":"2026-04-29T16:47:10","modified_gmt":"2026-04-29T11:17:10","slug":"python-glossary","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/python-glossary\/","title":{"rendered":"59 Python Glossary of Terms You Must Know"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:149,&quot;href&quot;:&quot;https:\\\/\\\/www.python.org&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251206090101\\\/https:\\\/\\\/www.python.org\\\/&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-06 12:20:59&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2025-12-09 12:44:48&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2025-12-12 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06:27:39&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-24 07:06:36&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-27 07:30:50&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-05-30 08:47:47&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-02 09:37:18&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-05 09:43:29&quot;,&quot;http_code&quot;:206}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-05 09:43:29&quot;,&quot;http_code&quot;:206},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<h3>1. Python Glossary<\/h3>\n<p>In this Python Glossary tutorial, we list important terminologies of Python that you will come across as you proceed to embrace it. Let\u2019s begin.<\/p>\n<div id=\"attachment_10470\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Python-Glossary-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-10470\" class=\"wp-image-10470 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Python-Glossary-01.jpg\" alt=\"Python Glossary\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Python-Glossary-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Python-Glossary-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Python-Glossary-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Python-Glossary-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/03\/Python-Glossary-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-10470\" class=\"wp-caption-text\">Python Glossary<\/p><\/div>\n<p><strong>2. &gt;&gt;&gt;<\/strong><br \/>\nThis is the default prompt of the Python interactive shell. We have seen this a lot in our examples.<\/p>\n<p><strong>3. \u2026<\/strong><\/p>\n<p>The default prompt of the Python interactive shell when entering code under an indented block or within a pair of matching delimiters. Delimiters may be parentheses, curly braces, or square brackets.<br \/>\nThis is also called the ellipsis object.<\/p>\n<p><strong>4. 2to3<\/strong><\/p>\n<p>While most of the applications existing today have their base in Python 2.x, the future belongs to Python 3.x. But 2.x code isn\u2019t completely compatible with 3.x. Interestingly, we have a tool available that will help us convert Python 2.x code to Python 3.x.<\/p>\n<p>2to3 handles the incompatibilities, detecting them by parsing the source and traversing the parse tree. The standard library has this as lib2to3.<\/p>\n<p><strong>5. Abstract Base Class<\/strong><\/p>\n<p>An abstract base class provides a way to define interfaces. This way, it complements duck typing. For this, we have the module abc. It introduces virtual subclasses (classes that are recognized by isinstance() and issubclass(), but do not inherit from another class.<\/p>\n<p>Python has several built-in ABCs for data structures (use the collections.abc module), numbers (use the numbers module), or streams (use the io module). You can also import finders and loaders (use the importlib.abc module). And to create our own ABCs, we use the abc module.<\/p>\n<p><strong>6. Python Argument<\/strong><\/p>\n<p>An argument is a value we pass to a function or a method when calling it. In Python, we have the following kinds of arguments:<\/p>\n<p><strong>a. Default Arguments<\/strong><\/p>\n<p>When defining a function, we can provide default values for arguments. This way, when we call it without any missing arguments, the default values will fill in for them. Default arguments can only follow non-default ones.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; def sayhello(name='User'):\r\n          print(f\"Hello, {name}\")\r\n&gt;&gt;&gt; sayhello('Ayushi')<\/pre>\n<p>Hello, Ayushi<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; sayhello()<\/pre>\n<p>Hello, User<\/p>\n<p><strong>b. Keyword Arguments Python<\/strong><\/p>\n<p>Keyword arguments pertain to calling a function. When we then call the function, we can pass it arguments in any order.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; def subtract(a,b):\r\n<\/pre>\n<p>return b-a<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; subtract(3,2)<\/pre>\n<p>-1<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; subtract(b=2,a=3)<\/pre>\n<p>-1<br \/>\n<strong>c. Arbitrary Arguments<\/strong><\/p>\n<p>When we don\u2019t know how many arguments we\u2019ll get, we use an asterisk to denote an arbitrary argument.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; def sum_all(*nums):\r\n      total=0\r\n      for i in nums:\r\n             total+=i\r\n<\/pre>\n<p>return total<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; sum_all(1,2,3,4)<\/pre>\n<p>10<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; sum_all(1,2,3)<\/pre>\n<p>6<\/p>\n<p><strong>d. Positional Arguments Python<\/strong><\/p>\n<p>These are regular arguments that aren\u2019t keyword arguments. Python Positional Argument Example.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; def add(a,b):\r\n<\/pre>\n<p>return a+b<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; add(3,4)<\/pre>\n<p>7<br \/>\nWe use a * before an iterable if we must pass it as an argument to a function.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; add(*(3,4))<\/pre>\n<p>7<br \/>\nFor more on arguments to functions, read on <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-function-arguments\/\">Python Function Arguments<\/a><\/strong>.<\/p>\n<p><strong>7. Asynchronous Context Manager<\/strong><\/p>\n<p>ACM is an object that controls the environment observed in an async with statement. It does so by defining __aenter__() and __aexit__().<\/p>\n<p><strong>8. Asynchronous Python Generator<\/strong><\/p>\n<p>We have seen about<strong> <a href=\"https:\/\/data-flair.training\/blogs\/python-generators\/\">Generators in Python<\/a><\/strong>. They let us yield one object at a time.<br \/>\nAn asynchronous generator is a function that returns an asynchronous generator iterator. We define it with \u2018async def\u2019, and it contains \u2018yield\u2019 expressions to produce a series of values. We can use these values in an async for-loop.<\/p>\n<p>Such an asynchronous generator function may contain await expressions, and async for and async with statements.<\/p>\n<p><strong>9. Asynchronous Generator Iterator<\/strong><\/p>\n<p>An asynchronous generator function creates an asynchronous generator iterator.<br \/>\nWhen we call this iterator using the __anext__() method, it returns an awaitable object. This object executes the function\u2019s body until the next yield expression.<\/p>\n<p>Actually, each yield suspends processing temporarily. It remembers the location execution state, and the local variables and the pending try statements. On resuming with another awaitable returned by __anext__(), the generator iterator picks up where it left off.<\/p>\n<p><strong>10. Asynchronous Iterable<\/strong><\/p>\n<p>It is an object that we can use in an async for statement. It must return an asynchronous iterator from its __aiter__() method.<br \/>\nAny Doubt yet in the Python Glossary? Please Comment.<\/p>\n<p><strong>11. Asynchronous Iterator<\/strong><\/p>\n<p>An asynchronous iterator is an object that implements __aiter__() and __anext__() methods. __anext__() must return an awaitable object. async for resolves the awaitable returned from the iterator\u2019s __anext__() method until it raises a StopAsyncIteration exception.<\/p>\n<p><strong>12. Attribute<\/strong><\/p>\n<p>An attribute is a value an object holds. We can access an object\u2019s attributes using the dot operator (.). In our examples, we have done this as follows:<\/p>\n<p>orange.color<\/p>\n<p><strong>13. Awaitable<\/strong><\/p>\n<p>Any object in Python that we can use in an await expression is an awaitable. It can be a coroutine or any object with an __await__() method.<\/p>\n<p><strong>14. BDFL<\/strong><br \/>\nWho other than Guido Van Rossum, the creator of Python, deserves to be called Benevolent Dictator For Life?<\/p>\n<p><strong>15. Binary File<\/strong><\/p>\n<p>A file object that is able to read and write bytes-like objects is a binary file. When we open a file in a binary mode, we use the modes \u2018rb\u2019, \u2018wb\u2019, or \u2018rb+\u2019.<br \/>\nMore on <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-file\/\">File I\/O<\/a><\/strong>.<\/p>\n<p><strong>16. Bytes-like Object<\/strong><\/p>\n<p>Any object that supports the Buffer Protocol and is able to export a C-contiguous buffer is a bytes-like object. Examples include bytes, bytearray, and array.array objects. It also includes many common memoryview objects.<br \/>\nWe can use such objects for operations that deal with binary data (compression, saving to a binary file, sending over a socket, and more)<\/p>\n<p><strong>17. Bytecode<\/strong><\/p>\n<p>As you know, Python compiles its source code into bytecode. It is the internal representation of a Python program in the CPython interpreter. When we talked earlier about .pyc files, we mentioned that bytecode is cached in them. This lets the files execute faster the second time since they don\u2019t need to recompile.<\/p>\n<p>In essence, bytecode is like an intermediate language that runs on a virtual machine. This virtual machine converts it into machine code for the machine to actually execute it on.<\/p>\n<p>However, one bytecode will not run on a different virtual machine.<br \/>\nIf you\u2019re interested in finding out about bytecode instructions, you can refer to the official documentation for the dis module.<\/p>\n<p><strong>18. Python Class<\/strong><\/p>\n<p>A class, in Python, is a template for creating user-defined objects. It is an abstract data type, and acts as a blueprint for objects of a kind while having no values itself.<br \/>\nTo learn how to create and use a class, refer to <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-classes\/\" target=\"_blank\" rel=\"noopener\">Classes in Python<\/a><\/strong>.<\/p>\n<p><strong>19. Coercion<\/strong><\/p>\n<p>When we carry out operations like 2+3.7, the interpreter implicitly converts one data type to another. Here, it converts 2 to 2.0 (int to float), and then adds, to it, 3.7. This is called coercion, and without it, we would have to explicitly do it this way:<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; float(2)+3.7<\/pre>\n<p>5.7<\/p>\n<p><strong>20. Complex Number<\/strong><\/p>\n<p>A complex number is made of real and imaginary parts. In Python, we use \u2018j\u2019 to represent the imaginary part.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; type(2+3.7j)<\/pre>\n<p>&lt;class &#8216;complex&#8217;&gt;<br \/>\nAn imaginary number is a real multiple of -1(the imaginary unit). To work with complex equivalents of the math module, we use cmath. For more on complex numbers, read up on <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-number-types-conversion\/\">Python Numbers<\/a>.<\/strong><br \/>\nThese Python Glossary terms are very important to know before you dive into learning Python.<\/p>\n<p><strong>21. Context Manager<\/strong><\/p>\n<p>The context manager is an object that controls the environment observed in a with-statement. It does so with the __enter__() and __exit__() methods.<\/p>\n<p><strong>22. Coroutine<\/strong><\/p>\n<p>A subroutine enters at one point and exits at another. A coroutine is more generalized, in that it can enter, exit, and resume at many different points. We implement them with the async def statement.<\/p>\n<p><strong>23.\u00a0 Coroutine Function<\/strong><\/p>\n<p>A coroutine function is simply a function that returns a coroutine object. We may define such a function with the async def statement, and it may contain the keywords await, async for, and async with.<\/p>\n<p><strong>24. CPython<\/strong><\/p>\n<p>CPython is the canonical implementation of Python in C. It is the one distributed on python.org.<\/p>\n<p><strong>25. Python Decorator<\/strong><\/p>\n<p>A decorator is a function that returns another function, or wraps it. It adds functionality to it without modifying it. For a simple, detailed view of decorators, refer to <a href=\"https:\/\/data-flair.training\/blogs\/decorators-in-python\/\">Python Decorators<\/a>.<\/p>\n<p><strong>26. Descriptor<\/strong><\/p>\n<p>If an object defines methods __get__(), __set__(), or __delete__(), we can call it a descriptor. On looking up an attribute from a class, the descriptor attribute\u2019s special binding behavior activates. Using a.b looks up the object \u2018b\u2019 in the class dictionary for \u2018a\u2019. If \u2018b\u2019 is a descriptor, then the respective descriptor method is called.<\/p>\n<p><strong>27. Python Dictionary<\/strong><\/p>\n<p>A dictionary is an associative array that holds key-value pairs. Think of a real-life dictionary. Any object with __hash__() and __eq__() methods can be a key.<\/p>\n<p><strong>28. Dictionary View<\/strong><br \/>\nA dictionary view is an object returned from dict.keys(), dict.values(), or dict.items(). This gives us a dynamic view on the dictionary\u2019s entries. So, when the dictionary changes, the view reflects those changes.<\/p>\n<p><strong>29. Docstring<\/strong><\/p>\n<p>A docstring is a string literal that we use to explain the functionality of a class, function, or module. It is the first statement in any of these constructs, and while the interpreter ignores them, it retains them at runtime. We can access it using the __doc__ attribute of such an object. You can find out more about docstrings in<strong> <a href=\"https:\/\/data-flair.training\/blogs\/python-comment\/\">Python Comments<\/a><\/strong>.<\/p>\n<p><strong>30. Duck-Typing<\/strong><\/p>\n<p>We keep saying that Python follows duck-typing. But what does this mean? This means that Python does not look at an object\u2019s type to determine if it has the right interface. It simply calls or uses the method or attribute. \u201cIf it looks and quacks like a duck, it must be a duck.\u201d<\/p>\n<p>This improves flexibility by allowing polymorphic substitution. With duck-typing, you don\u2019t need tests like type() or isinstance(); instead, you use hasattr() tests or EAFP programming.<\/p>\n<p><strong>31. EAFP Programming<\/strong><\/p>\n<p>EAFP stands for Easier to Ask for Forgiveness than Permission.<\/p>\n<p>This means that Python assumes the existence of valid keys or attributes and catches exceptions on the falsity of the assumption. When we have too many try and except statements in our code, we can observe this nature of Python.<br \/>\nOther languages, like C, follow LBYL (Look Before You Leap).<\/p>\n<p><strong>32. Python Expression<\/strong><br \/>\nAn expression is a piece of code that we can evaluate to a value. It is an aggregation of expression elements like literals, names, attribute access, operators, or function calls. All of these return a value. An if-statement is not an expression, and neither is an assignment, because these do not return a value.<\/p>\n<p><strong>33. Extension Module<\/strong><\/p>\n<p>An extension module is one written in C or C++, using Python\u2019s C API to interact with the core and with user code.<\/p>\n<p><strong>34. f-string<\/strong><\/p>\n<p>An f-string is a formatted string literal. To write these, we precede a string with the letter \u2018f\u2019 or \u2018F\u2019. This lets us put values into a string.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; name,surname='Ayushi','Sharma'\r\n&gt;&gt;&gt; print(f\"I am {name}, and I am a {surname}\")<\/pre>\n<p>I am Ayushi, and I am a Sharma<br \/>\nFor more on f-strings, read up on<strong> <a href=\"https:\/\/data-flair.training\/blogs\/python-strings\/\">Python Strings<\/a><\/strong>.<\/p>\n<p><strong>35. File Object<\/strong><\/p>\n<p>A file object, in Python, is an object that exposes a file-oriented API to an underlying resource. Such an API has methods such as read() and write().<\/p>\n<p>We also call them file-like objects or streams, and have three categories:<\/p>\n<ul>\n<li>Raw binary files<\/li>\n<li>Buffered binary files<\/li>\n<li>Text files<\/li>\n<\/ul>\n<p>The canonical way to create a file object is to use the open() function. For help with reading and writing files, refer to <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-read-and-write-file\/\" target=\"_blank\" rel=\"noopener\">Reading and Writing Files in Python<\/a><\/strong>.<\/p>\n<p><strong>36. File-Like Object<\/strong><\/p>\n<p>As we said earlier, it is a synonym for file objects.<\/p>\n<p><strong>37. Finder<\/strong><\/p>\n<p>The finder is an object that attempts to find the loader for a module that we are importing.<br \/>\nWith Python 3.3 and above, we have two types of finders:<\/p>\n<p>Meta path finders- to use with sys.meta_path<br \/>\nPath entry finders- to use with sys.path_hooks<\/p>\n<p><strong>38. Floor Division<\/strong><\/p>\n<p>Floor division is a division that rounds the result down to the nearest integer. For this, we use the \/\/ operator.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; 18\/\/4<\/pre>\n<p>4<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; -18\/\/4    #-4.5 is rounded down to -5<\/pre>\n<p>-5<br \/>\nThese are some of the terminologies from our Python Glossary. We have Python Glossary Part II as well for more Python Glossaries. The link is provided at the end of this article.<\/p>\n<p><strong>39. Python Function<\/strong><\/p>\n<p>A function is a sequence of statements that may return a value to the caller. It may take zero or more arguments. For more on functions, read up on <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-functions\/\" target=\"_blank\" rel=\"noopener\">Functions in Python<\/a><\/strong>.<\/p>\n<p><strong>40. Function Annotation<\/strong><\/p>\n<p>An annotation to a function is an arbitrary metadata value associated with a parameter or return value. We can access a function\u2019s annotations using the __annotations__ attribute. And while Python itself does not assign a meaning to an annotation, third-party libraries or tools make use of them.<\/p>\n<p><strong>41. __future__<\/strong><\/p>\n<p>Interestingly, in Python, we have a pseudo-module available that lets us enable new language features that aren\u2019t yet compatible with the current interpreter.<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; import __future__\r\n&gt;&gt;&gt; __future__.division<\/pre>\n<p>_Feature((2, 2, 0, &#8216;alpha&#8217;, 2), (3, 0, 0, &#8216;alpha&#8217;, 0), 8192)<\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; __future__.absolute_import<\/pre>\n<p>_Feature((2, 5, 0, &#8216;alpha&#8217;, 1), (3, 0, 0, &#8216;alpha&#8217;, 0), 16384)<\/p>\n<p><strong>42. Garbage Collection<\/strong><\/p>\n<p>Memory must be freed when it isn\u2019t needed anymore. Using reference counting and a cyclic garbage collector that can detect and break reference cycles, Python collects its garbage. We can use the gc module additionally.<\/p>\n<p><strong>43. Python Generator<\/strong><\/p>\n<p>A generator is a function that \u2018yields\u2019 values one by one. \u00a0It returns a generator iterator. We can use this function with a for-loop to retrieve one value at a time.<br \/>\nFor more on generators, refer to <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-generators\/\" target=\"_blank\" rel=\"noopener\">Generators in Python<\/a><\/strong>.<\/p>\n<p><strong>44. Generator Iterator<\/strong><\/p>\n<p>A generator iterator is an object created by a generator function.<\/p>\n<p><strong>45. Generator Expression<\/strong><\/p>\n<p>It is an expression that returns an iterator. Below is an example of the same.<br \/>\n&gt;&gt;&gt; sum(i**2 for i in range(7))<br \/>\n91<\/p>\n<p><strong>46. Generic Function<\/strong><\/p>\n<p>A generic function is made of multiple functions that implement the same operation for different types. The dispatch algorithm decides which implementation to use during a call.<\/p>\n<p><strong>47. GIL<\/strong><\/p>\n<p>GIL stands for Global Interpreter Lock. This is the mechanism that the CPython interpreter uses to ensure that only one thread executes Python bytecode at a time. This makes the object model implicitly safe against concurrent access, and this simplifies CPython.<\/p>\n<p><strong>48. Hashable<\/strong><\/p>\n<p>If an object has a fixed hash value for its entire lifetime and is comparable to other objects, it is hashable. Two equal hashable objects have the same hash values.<\/p>\n<p>While a dictionary itself is unhashable, it cannot hold unhashable types like itself. In fact, all immutable types are hashable. Mutables like lists are not dictionaries. User-defined objects are hashable.<\/p>\n<p><strong>49. IDLE<\/strong><\/p>\n<p>IDLE is an Integrated Development Environment for Python. It is a basic editor and interpreter environment that ships with Python.<\/p>\n<p><strong>50. Immutable<\/strong><\/p>\n<p>Any object with a fixed value is an immutable. Examples include numbers, strings, and tuples. If you must change a value, you need to create a new object. In places where we need a constant hash value, like a key in a dictionary, we use immutables.<\/p>\n<p><strong>51. Import Path<\/strong><\/p>\n<p>A list of locations searched by the path-based finder to import modules. During an import, this list comes from sys.path. For subpackages, it may come from the parent package\u2019s __path__ attribute.<\/p>\n<p><strong>52. Importing<\/strong><\/p>\n<p>Importing is the process through which we make the Python code in one module available to another.<\/p>\n<p><strong>53. Importer<\/strong><\/p>\n<p>The importer is an object that finds and loads a module. Hence, it is both- a finder and a loader object.<\/p>\n<p><strong>54. Interactive<\/strong><\/p>\n<p>Being of an interpreted nature, Python lets you enter statements\/expressions at the interpreter prompt, and immediately execute them and see results.<\/p>\n<p><strong>55. Interpreted<\/strong><\/p>\n<p>We couldn\u2019t highlight this more when we say Python is an interpreted language. However, because it does have a bytecode compiler, the distinction is a bit blurry. The source files can run without explicitly creating an executable. While this makes Python faster to develop\/debug, it often results in slower execution.<\/p>\n<p><strong>56. Interpreter Shutdown<\/strong><\/p>\n<p>When we shut down the interpreter, it gradually releases all allocated resources. These include modules and different critical internal structures. Alongside, it makes several calls to the garbage collector.<\/p>\n<p>This may trigger execution of code in user-defined destructors or in weakref callbacks. Since the resources it relies on may not function anymore during the shutdown phase, the code executed can encounter various exceptions.<\/p>\n<p><strong>57. Python Iterable<\/strong><\/p>\n<p>Any object that can return its members one at a time is an iterable. Examples include lists, strings, tuples, dicts, and file objects.<br \/>\nFor more on iterables, read up on <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-iterables\/\" target=\"_blank\" rel=\"noopener\">Python Iterables<\/a><\/strong>.<\/p>\n<p><strong>58. Python Iterator<\/strong><\/p>\n<p>An iterator is an object that represents a stream of data. We can define an iterator using the iter() function\/method, and get one object at a time with the next() function\/method.<br \/>\nFor a detailed introduction to iterators, refer to <strong><a href=\"https:\/\/data-flair.training\/blogs\/python-iterator\/\" target=\"_blank\" rel=\"noopener\">Python Iterators<\/a><\/strong>.<\/p>\n<p><strong>59. Key Function<\/strong><\/p>\n<p>It is a callable that returns a value that we can use for sorting or ordering. We also call it a collation function. Functions like max(), min(), and sorted() make use of them.<\/p>\n<p><strong>60. Keyword Argument<\/strong><\/p>\n<p>Refer to section 6 for this.<\/p>\n<p>This is all about the Python Glossary Part I. For more Python Glossary, see Python Glossary Part II.<\/p>\n<h3><strong>Conclusion<br \/>\n<\/strong><\/h3>\n<p>Here, we discussed 59 common Python glossary terms we see in Python. Stay tuned for more, and feel free to ask a doubt. If you have any queries regarding the Python Glossary Tutorial, Please Comment. We hope you like the Python Glossary Tutorial.<\/p>\n<p>You have just leveled up your Python knowledge. Knowing these terms will save you a lot of searching time later. Keep this list of terms with you as a cheatsheet. We&#8217;re excited to see you use your new &#8216;Python-speak&#8217; in the next lesson.<\/p>\n<p><a href=\"https:\/\/www.python.org\/\"><strong>Reference &#8211; Python\u00a0<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1. Python Glossary In this Python Glossary tutorial, we list important terminologies of Python that you will come across as you proceed to embrace it. Let\u2019s begin. 2. &gt;&gt;&gt; This is the default prompt&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":36454,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[46],"tags":[131,1207,1208,1209,1210,1224,1326,1987,2257,2258,2626,2798,10366,10414,10565,10566],"class_list":["post-10467","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-2to3","tag-asynchronous-context-manager","tag-asynchronous-generator-iterator","tag-asynchronous-iterable","tag-asynchronous-python-generator","tag-attribute","tag-awaitable","tag-binary-file","tag-bytecode","tag-bytes-like-object","tag-coercion","tag-complex-number","tag-python-argument","tag-python-class","tag-python-glossary","tag-python-glossary-of-terms"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>59 Python Glossary of Terms You Must Know - 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