{"id":78022,"date":"2020-04-06T17:21:39","date_gmt":"2020-04-06T11:51:39","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=78022"},"modified":"2020-04-06T17:21:39","modified_gmt":"2020-04-06T11:51:39","slug":"terminologies-in-python","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/terminologies-in-python\/","title":{"rendered":"Terminologies in Python – Don’t call yourself a Python Developer if you don’t know these terms"},"content":{"rendered":"

Hello There!<\/p>\n

The Terminologies in Python<\/strong> article by TechVidvan<\/strong> will guide you towards some amazing terminologies<\/strong> in python.<\/p>\n

So, let\u2019s go ahead and learn about them.<\/p>\n

Terminologies in Python<\/h3>\n

\"\"<\/a><\/h4>\n

1. lambda<\/h4>\n

\u2018lambda\u2019<\/strong> is a keyword<\/strong> that lets you create lambda functions<\/strong>. These are anonymous functions used to perform simple computations<\/strong>.<\/p>\n

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

>>> x = lambda a, b: a + b\n>>> x(5, 6)<\/pre>\n
Now, x<\/strong> is a function<\/strong> that takes two arguments<\/strong> and returns their sum<\/strong>.<\/p>\n
2. list<\/h4>\n
A list in Python is an ordered<\/strong>, mutable collection<\/strong> of objects<\/strong>. These objects can be of different types<\/strong>.<\/p>\n
Below is an example of a valid list<\/strong> in Python:<\/p>\n
list1 = [\u2018Techvidvan\u2019, 2, 9.0]<\/pre>\n
3. List comprehension<\/h4>\n
List comprehensions provide a way to create lists<\/strong> in a concise manner<\/strong>. We do this by writing<\/strong> an expression inside<\/strong> square brackets<\/strong> and assigning it<\/strong> to a variable<\/strong>.<\/p>\n
Example,<\/strong><\/p>\n
list1 = [i for i in range(10)]<\/pre>\n
Output:<\/strong><\/p>\n
>>> list1
\n[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]<\/p>\n
4. Literals<\/h4>\n
Literals are data items<\/strong> that have a fixed value<\/strong>. Python allows several kinds of literals<\/strong> like string<\/strong>, numeric<\/strong>, boolean<\/strong> etc.<\/p>\n
5. List length<\/h4>\n
List length refers to the size<\/strong> of a list<\/strong>, that is, the number of elements in the list<\/strong>.<\/p>\n
6. loader<\/h4>\n
A loader is an object<\/strong> that loads<\/strong> a module<\/strong> that is being imported<\/strong>.<\/p>\n
7. loop<\/h4>\n
A loop is used for iterating<\/strong> over a sequence<\/strong> (like list<\/strong>, tuple<\/strong>, string<\/strong>, dictionary<\/strong>, set<\/strong>). Python offers two loops: for<\/strong> and while<\/strong>.<\/p>\n
8. Methods<\/h4>\n
Python Methods are functions associated<\/strong> with an object<\/strong>. Methods of an object operate<\/strong> on the data<\/strong> of that object<\/strong>. We call a method<\/strong> on an object<\/strong> using the dot operator<\/strong>.<\/p>\n
For example, if obj<\/strong> is an object<\/strong> and med()<\/strong> is its method<\/strong>, we call it as obj.med()<\/strong>.<\/p>\n
9. module<\/h4>\n
A module is a file consisting<\/strong> of Python code<\/strong>. This module can be imported<\/strong> and the code<\/strong> within<\/strong> can be reused<\/strong> in another Python program<\/strong>.<\/p>\n
10. MRO<\/h4>\n
MRO stands for Method Resolution Order<\/strong>. It defines the order<\/strong> in which Python Interpreter<\/strong> looks for a method<\/strong> in a hierarchy<\/strong> of classes<\/strong>.<\/p>\n
11. mutable<\/h4>\n
A mutable data type<\/strong> is one that can change<\/strong> its value<\/strong> during the program execution<\/strong>.<\/p>\n
12. Nested loop<\/h4>\n
Nested loops refer to a loop within another loop<\/strong>. For each pass<\/strong> of the outer loop<\/strong>, the inner loop<\/strong> executes<\/strong> completely before moving on<\/strong> to the next pass<\/strong> of the outer loop<\/strong>.<\/p>\n
13. Object<\/h4>\n
An object is a collection of data<\/strong> and methods<\/strong>. Objects model real-world entities<\/strong> in object-oriented programming<\/strong>.<\/p>\n
14. Object-oriented<\/h4>\n
Object-oriented programming is a programming paradigm<\/strong> that is based on objects<\/strong>. The emphasis in object-oriented programming is on data<\/strong> rather than procedure<\/strong>.<\/p>\n
15. parameter<\/h4>\n
A parameter is a variable<\/strong> that is specified in a function definition<\/strong> inside<\/strong> the parenthesis<\/strong>. Parameter denotes the argument values<\/strong> a function can accept<\/strong>.<\/p>\n
16. pass<\/h4>\n
Pass keyword is used to write a pass statement<\/strong>. Pass statement is used when there is a syntactical<\/strong> need of a statement<\/strong> but your program\u2019s logic<\/strong> doesn\u2019t need one<\/strong>.<\/p>\n
17. PEP 8<\/h4>\n
PEP 8<\/strong>, that is, Python Enhancement Proposal<\/strong> is a style guide<\/strong> of Python code<\/strong>. It includes guidelines<\/strong> and rules<\/strong> that define how to write<\/strong> better<\/strong> looking Python code<\/strong>.<\/p>\n
18. Pythonic<\/h4>\n
A piece of code<\/strong> is said to be Pythonic<\/strong> if it is not only syntactically correct<\/strong> but also follows<\/strong> the guidelines<\/strong> and conventions<\/strong> of the Python community<\/strong>.<\/p>\n
19. Set<\/h4>\n
A set is an unordered collection of data<\/strong> that allows<\/strong> only unique values<\/strong>. All operations that can be performed<\/strong> on a mathematical set<\/strong> can be performed on a Python set<\/strong> as well.<\/p>\n
20. Slicing<\/h4>\n
It refers to accessing a particular part<\/strong> of a sequence<\/strong>, like lists<\/strong>, tuples<\/strong>, and strings<\/strong>. Slicing can be used to modify<\/strong> and manipulate parts<\/strong> of mutable sequences<\/strong> like lists<\/strong>.<\/p>\n
A slice of a list is accessed as follows:<\/strong><\/p>\n
<listname>[start : stop]<\/pre>\n
In the below example we modify the list slice starting at index 3<\/strong> and ending<\/strong> at index 4<\/strong>. We can see the change<\/strong> in the original list<\/strong>.<\/p>\n
21. String<\/h4>\n
A string is a sequence of characters<\/strong>. A string in Python<\/strong> is immutable<\/strong> and is written within single or double-quotes<\/strong>.<\/p>\n
Python does not support<\/strong> a character data type<\/strong>, so a character<\/strong> in Python is just a string of length 1<\/strong>.<\/p>\n
Example,<\/strong><\/p>\n
>>> string1 = \u201cTechvidvan\u201d<\/pre>\n
22. Statement<\/h4>\n
A statement in Python specifies<\/strong> an instruction<\/strong> given to the machine<\/strong> to perform<\/strong> a particular task<\/strong>. It forms the smallest executable unit<\/strong> within a Python program.<\/p>\n
23. try<\/h4>\n
Python allows catching exceptions<\/strong> that may occur in the code written<\/strong> within<\/strong> a try block<\/strong>. The exceptions caught<\/strong> can be handled<\/strong> by an except clause <\/strong>written right below<\/strong> the try block<\/strong>.<\/p>\n
24. tuple<\/h4>\n
A tuple is an ordered<\/strong>, immutable collection of data<\/strong>. A tuple, once defined, cannot change<\/strong> the value<\/strong> of its elements<\/strong> during the program execution<\/strong>.<\/p>\n
25. type<\/h4>\n
The type of object<\/strong> defines<\/strong> the kind of object<\/strong> it is. It can be found out using the type() method<\/strong>.<\/p>\n
26. Variables<\/h4>\n
A variable in Python is a named memory location<\/strong> that stores a value<\/strong>. The variable is capable<\/strong> of changing its value<\/strong> during the program execution<\/strong>.<\/p>\n
27. Virtual machine<\/h4>\n
The Python Virtual machine is responsible for executing<\/strong> the bytecode<\/strong> generated<\/strong> from the Python source code<\/strong>.<\/p>\n
28. yield<\/h4>\n
A yield expression returns<\/strong> a value<\/strong> from a generator function<\/strong> in Python<\/p>\n
29. Zen of Python<\/h4>\n
The \u2018import this\u2019<\/strong> command prints<\/strong> out the philosophy<\/strong> in which Python believes.<\/p>\n
Go to the Python shell and type this:<\/p>\n
<\/p>\n
Summary<\/h3>\n
Here we come to the end of our tutorial.<\/p>\n
In this article we explored some of the terminologies in python<\/strong>. Now that you know all the important Python Terminologies, we are sure that through this article you will get a better understanding of Python<\/strong>, its syntax<\/strong> and its working<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"
Hello There! The Terminologies in Python article by TechVidvan will guide you towards some amazing terminologies in python. So, let\u2019s go ahead and learn about them. Terminologies in Python 1. lambda \u2018lambda\u2019 is a...<\/p>\n","protected":false},"author":1,"featured_media":78082,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1053],"tags":[2242,2243,2244,2245,2246,2297,2298],"class_list":["post-78022","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","tag-terminology-python","tag-terms-of-python"],"yoast_head":"\nTerminologies in Python - Don't call yourself a Python Developer if you don't know these terms - TechVidvan<\/title>\n<meta name=\"description\" content=\"Make yourself aware with the concept of Terminologies in Python. 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