{"id":3586,"date":"2023-12-20T12:03:31","date_gmt":"2023-12-20T09:03:31","guid":{"rendered":"https:\/\/potentsky.com\/fse-staging\/?page_id=3586"},"modified":"2025-07-26T16:14:24","modified_gmt":"2025-07-26T13:14:24","slug":"3586-2","status":"publish","type":"page","link":"https:\/\/potentsky.com\/fse-staging\/3586-2\/","title":{"rendered":"Glossary Python_Data Structures"},"content":{"rendered":"\n
<\/div>\n\n\n\nGlossary: Python Data Structures\nWelcome! This alphabetized glossary contains many of the terms in this course. This comprehensive glossary also includes additional industry-recognized terms not used in course videos. These terms are important for you to recognize when working in the industry, participating in user groups, and participating in other certificate programs.\n\nTerm\tDefinition\nAliasing\tAliasing refers to giving another name to a function or a variable.\nAmpersand\tA character typically “&” standing for the word “and.”\nCompound elements\tCompound statements contain (groups of) other statements; they affect or control the execution of those other statements in some way.\nDelimiter\tA delimiter in Python is a character or sequence of characters used to separate or mark the boundaries between elements or fields within a larger data structure, such as a string or a file.\nDictionaries\tA dictionary in Python is a data structure that stores a collection of key-value pairs, where each key is unique and associated with a specific value.\nFunction\tA function is a block of code, defining a set procedure, which is executed only when it is called.\nImmutable\tImmutable Objects are of in-built datatypes like int, float, bool, string, Unicode, and tuple. In simple words, an immutable object can’t be changed after it is created.\nIntersection\tThe intersection of two sets is a new set containing only the elements that are present in both sets.\nKeys\tThe keys () method in Python Dictionary, returns a view object that displays a list of all the keys in the dictionary in order of insertion using Python.\nLists\tA list is any list of data items, separated by commas, inside square brackets.\nLogic operations\tIn Python, logic operations refer to the use of logical operators such as “and,” “or,” and “not” to perform logical operations on Boolean values (True or False).\nMutable\tMutable objects in Python are objects whose values can be changed after they are created. These objects allow modifications such as adding, removing, or altering elements without creating a new object.\nNesting\tA nested function is simply a function within another function and is sometimes called an “inner function”.\nRatings in python\tRatings in Python typically refer to a numerical or qualitative measure assigned to something to indicate its quality, performance, or value.\nSet operations\tSet operations in Python refer to mathematical operations performed on sets, which are unordered collections of unique elements.\nSets in python\tA set is an unordered collection of unique elements.\nSyntax\tThe rules that define the structure of the language for python is called its syntax.\nTuples\tThese are used store multiple items in a single variable.\nType casting\tIn python, this is converting one data type to another.\nVariables\tIn python, a variable is a symbolic name or identifier used to store and manipulate data. Variables serve as containers for values, and these values can be of various data types, including numbers, strings, lists, and more.\nVenn diagram\tA Venn diagram is a graphical representation that uses overlapping circles to illustrate the relationships and commonalities between sets or groups of items.\nVersatile data\tVersatile data, in a general context, refers to data that can be used in multiple ways, is adaptable to different applications or purposes, and is not restricted to a specific use case.\n\n\n\n
<\/div>\n\n\n\nCheat Sheet: Python Data Structures Part-2\n\nDictionaries\n\nPackage\/Method\tDescription\tCode Example\nCreating a Dictionary\t\nA dictionary is a built-in data type that represents a collection of key-value pairs. Dictionaries are enclosed in curly braces {}.\n\nExample:\n\n1\n2\ndict_name = {} #Creates an empty dictionary\nperson = { “name”: “John”, “age”: 30, “city”: “New York”}\n\nCopied!\n\nWrap Toggled!\nAccessing Values\t\nYou can access the values in a dictionary using their corresponding keys.\n\nSyntax:\n\n1\nValue = dict_name[“key_name”]\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\nname = person[“name”]\nage = person[“age”]\n\nCopied!\n\nWrap Toggled!\nAdd or modify\n\nInserts a new key-value pair into the dictionary. If the key already exists, the value will be updated; otherwise, a new entry is created.\n\nSyntax:\n\n1\ndict_name[key] = value\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\nperson[“Country”] = “USA” # A new entry will be created.\nperson[“city”] = “Chicago” # Update the existing value for the same key\n\nCopied!\n\nWrap Toggled!\ndel\t\nRemoves the specified key-value pair from the dictionary. Raises a KeyError if the key does not exist.\n\nSyntax:\n\n1\ndel dict_name[key]\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\ndel person[“Country”]\n\nCopied!\n\nWrap Toggled!\nupdate()\t\nThe update() method merges the provided dictionary into the existing dictionary, adding or updating key-value pairs.\n\nSyntax:\n\n1\ndict_name.update({key: value})\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nperson.update({“Profession”: “Doctor”})\n\nCopied!\n\nWrap Toggled!\nclear()\t\nThe clear() method empties the dictionary, removing all key-value pairs within it. After this operation, the dictionary is still accessible and can be used further.\n\nSyntax:\n\n1\ndict_name.clear()\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\ngrades.clear()\n\nCopied!\n\nWrap Toggled!\nkey existence\t\nYou can check for the existence of a key in a dictionary using the in keyword\n\nExample:\n\n1\n2\nif “name” in person:\n print(“Name exists in the dictionary.”)\n\nCopied!\n\nWrap Toggled!\ncopy()\t\nCreates a shallow copy of the dictionary. The new dictionary contains the same key-value pairs as the original, but they remain distinct objects in memory.\n\nSyntax:\n\n1\nnew_dict = dict_name.copy()\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\nnew_person = person.copy()\nnew_person = dict(person) # another way to create a copy of dictionary\n\nCopied!\n\nWrap Toggled!\nkeys()\t\nRetrieves all keys from the dictionary and converts them into a list. Useful for iterating or processing keys using list methods.\n\nSyntax:\n\n1\nkeys_list = list(dict_name.keys())\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nperson_keys = list(person.keys())\n\nCopied!\n\nWrap Toggled!\nvalues()\t\nExtracts all values from the dictionary and converts them into a list. This list can be used for further processing or analysis.\n\nSyntax:\n\n1\nvalues_list = list(dict_name.values())\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nperson_values = list(person.values())\n\nCopied!\n\nWrap Toggled!\nitems()\t\nRetrieves all key-value pairs as tuples and converts them into a list of tuples. Each tuple consists of a key and its corresponding value.\n\nSyntax:\n\n1\nitems_list = list(dict_name.items())\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\ninfo = list(person.items())\n\nCopied!\n\nWrap Toggled!\nSets\n\nPackage\/Method\tDescription\tCode Example\nadd()\tElements can be added to a set using the `add()` method. Duplicates are automatically removed, as sets only store unique values.\tSyntax:\n1\nset_name.add(element) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nfruits.add(“mango”)\n\nCopied!\n\nWrap Toggled!\nclear()\tThe `clear()` method removes all elements from the set, resulting in an empty set. It updates the set in-place.\tSyntax:\n1\nset_name.clear() \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nfruits.clear()\n\nCopied!\n\nWrap Toggled!\ncopy()\tThe `copy()` method creates a shallow copy of the set. Any modifications to the copy won’t affect the original set.\tSyntax:\n1\nnew_set = set_name.copy() \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nnew_fruits = fruits.copy()\n\nCopied!\n\nWrap Toggled!\nDefining Sets\tA set is an unordered collection of unique elements. Sets are enclosed in curly braces `{}`. They are useful for storing distinct values and performing set operations.\tExample:\n1\n2\n3\nempty_set = set() #Creating an Empty Set \nfruits = {“apple”, “banana”, “orange”}\ncolors = (“orange”, “red”, “green”)\n\nCopied!\n\nWrap Toggled!\nNote: These two sets will be used in the examples that follow.\n\ndiscard()\tUse the `discard()` method to remove a specific element from the set. Ignores if the element is not found.\tSyntax:\n1\nset_name.discard(element) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nfruits.discard(“apple”)\n\nCopied!\n\nWrap Toggled!\nissubset()\tThe `issubset()` method checks if the current set is a subset of another set. It returns True if all elements of the current set are present in the other set, otherwise False.\tSyntax:\n1\nis_subset = set1.issubset(set2)\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nis_subset = fruits.issubset(colors)\n\nCopied!\n\nWrap Toggled!\nissuperset()\tThe `issuperset()` method checks if the current set is a superset of another set. It returns True if all elements of the other set are present in the current set, otherwise False.\tSyntax:\n1\nis_superset = set1.issuperset(set2) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nis_superset = colors.issuperset(fruits)\n\nCopied!\n\nWrap Toggled!\npop()\tThe `pop()` method removes and returns an arbitrary element from the set. It raises a `KeyError` if the set is empty. Use this method to remove elements when the order doesn’t matter.\tSyntax:\n1\nremoved_element = set_name.pop() \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nremoved_fruit = fruits.pop()\n\nCopied!\n\nWrap Toggled!\nremove()\tUse the `remove()` method to remove a specific element from the set. Raises a `KeyError` if the element is not found.\tSyntax:\n1\nset_name.remove(element) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nfruits.remove(“banana”)\n\nCopied!\n\nWrap Toggled!\nSet Operations\tPerform various operations on sets: `union`, `intersection`, `difference`, `symmetric difference`.\tSyntax:\n1\n2\n3\n4\nunion_set = set1.union(set2) \nintersection_set = set1.intersection(set2) \ndifference_set = set1.difference(set2) \nsym_diff_set = set1.symmetric_difference(set2) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\n3\n4\ncombined = fruits.union(colors) \ncommon = fruits.intersection(colors) \nunique_to_fruits = fruits.difference(colors) \nsym_diff = fruits.symmetric_difference(colors)\n\nCopied!\n\nWrap Toggled!\nupdate()\tThe `update()` method adds elements from another iterable into the set. It maintains the uniqueness of elements.\tSyntax:\n1\nset_name.update(iterable) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\nfruits.update([“kiwi”, “grape”])\n\nCopied!\n\nWrap Toggled!\n\n\n\n\n
<\/div>\n\n\n\nPython Data Structures Cheat Sheet\n\nList\n\nPackage\/Method\tDescription\tCode Example\nappend()\tThe `append()` method is used to add an element to the end of a list.\tSyntax:\n1\nlist_name.append(element) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\nfruits = [“apple”, “banana”, “orange”] \nfruits.append(“mango”) print(fruits)\n\nCopied!\n\nWrap Toggled!\ncopy()\tThe `copy()` method is used to create a shallow copy of a list.\tExample 1:\n1\n2\n3\nmy_list = [1, 2, 3, 4, 5] \nnew_list = my_list.copy() print(new_list) \n# Output: [1, 2, 3, 4, 5]\n\nCopied!\n\nWrap Toggled!\ncount()\tThe `count()` method is used to count the number of occurrences of a specific element in a list in Python.\tExample:\n1\n2\n3\nmy_list = [1, 2, 2, 3, 4, 2, 5, 2] \ncount = my_list.count(2) print(count) \n# Output: 4\n\nCopied!\n\nWrap Toggled!\nCreating a list\tA list is a built-in data type that represents an ordered and mutable collection of elements. Lists are enclosed in square brackets [] and elements are separated by commas.\tExample:\n1\nfruits = [“apple”, “banana”, “orange”, “mango”]\n\nCopied!\n\nWrap Toggled!\ndel\tThe `del` statement is used to remove an element from list. `del` statement removes the element at the specified index.\tExample:\n1\n2\n3\nmy_list = [10, 20, 30, 40, 50] \ndel my_list[2] # Removes the element at index 2 print(my_list) \n# Output: [10, 20, 40, 50]\n\nCopied!\n\nWrap Toggled!\nextend()\tThe `extend()` method is used to add multiple elements to a list. It takes an iterable (such as another list, tuple, or string) and appends each element of the iterable to the original list.\tSyntax:\n1\nlist_name.extend(iterable) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\n3\n4\nfruits = [“apple”, “banana”, “orange”] \nmore_fruits = [“mango”, “grape”] \nfruits.extend(more_fruits) \nprint(fruits)\n\nCopied!\n\nWrap Toggled!\nIndexing\tIndexing in a list allows you to access individual elements by their position. In Python, indexing starts from 0 for the first element and goes up to `length_of_list – 1`.\tExample:\n1\n2\n3\n4\n5\nmy_list = [10, 20, 30, 40, 50] \nprint(my_list[0]) \n# Output: 10 (accessing the first element) \nprint(my_list[-1]) \n# Output: 50 (accessing the last element using negative indexing)\n\nCopied!\n\nWrap Toggled!\ninsert()\tThe `insert()` method is used to insert an element.\tSyntax:\n1\nlist_name.insert(index, element) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\n3\nmy_list = [1, 2, 3, 4, 5] \nmy_list.insert(2, 6) \nprint(my_list)\n\nCopied!\n\nWrap Toggled!\nModifying a list\tYou can use indexing to modify or assign new values to specific elements in the list.\tExample:\n1\n2\n3\n4\nmy_list = [10, 20, 30, 40, 50] \nmy_list[1] = 25 # Modifying the second element \nprint(my_list) \n# Output: [10, 25, 30, 40, 50]\n\nCopied!\n\nWrap Toggled!\npop()\t`pop()` method is another way to remove an element from a list in Python. It removes and returns the element at the specified index. If you don’t provide an index to the `pop()` method, it will remove and return the last element of the list by default\tExample 1:\n1\n2\n3\n4\n5\n6\n7\nmy_list = [10, 20, 30, 40, 50] \nremoved_element = my_list.pop(2) # Removes and returns the element at index 2 \nprint(removed_element) \n# Output: 30 \nprint(my_list) \n# Output: [10, 20, 40, 50] \n\nCopied!\n\nWrap Toggled!\nExample 2:\n\n1\n2\n3\n4\n5\n6\n7\nmy_list = [10, 20, 30, 40, 50] \nremoved_element = my_list.pop() # Removes and returns the last element \nprint(removed_element) \n# Output: 50 \nprint(my_list) \n# Output: [10, 20, 30, 40]\n\nCopied!\n\nWrap Toggled!\nremove()\tTo remove an element from a list. The `remove()` method removes the first occurrence of the specified value.\tExample:\n1\n2\n3\n4\nmy_list = [10, 20, 30, 40, 50] \nmy_list.remove(30) # Removes the element 30 \nprint(my_list) \n# Output: [10, 20, 40, 50]\n\nCopied!\n\nWrap Toggled!\nreverse()\tThe `reverse()` method is used to reverse the order of elements in a list\tExample 1:\n1\n2\n3\nmy_list = [1, 2, 3, 4, 5] \nmy_list.reverse() print(my_list) \n# Output: [5, 4, 3, 2, 1]\n\nCopied!\n\nWrap Toggled!\nSlicing\tYou can use slicing to access a range of elements from a list.\tSyntax:\n1\nlist_name[start:end:step] \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\nmy_list = [1, 2, 3, 4, 5] \nprint(my_list[1:4]) \n# Output: [2, 3, 4] (elements from index 1 to 3)\nprint(my_list[:3]) \n# Output: [1, 2, 3] (elements from the beginning up to index 2) \nprint(my_list[2:]) \n# Output: [3, 4, 5] (elements from index 2 to the end) \nprint(my_list[::2]) \n# Output: [1, 3, 5] (every second element)\n\nCopied!\n\nWrap Toggled!\nsort()\tThe `sort()` method is used to sort the elements of a list in ascending order. If you want to sort the list in descending order, you can pass the `reverse=True` argument to the `sort()` method.\tExample 1:\n1\n2\n3\n4\nmy_list = [5, 2, 8, 1, 9] \nmy_list.sort() \nprint(my_list) \n# Output: [1, 2, 5, 8, 9] \n\nCopied!\n\nWrap Toggled!\nExample 2:\n\n1\n2\n3\n4\nmy_list = [5, 2, 8, 1, 9] \nmy_list.sort(reverse=True) \nprint(my_list) \n# Output: [9, 8, 5, 2, 1]\n\nCopied!\n\nWrap Toggled!\nTuple\nPackage\/Method\tDescription\tCode Example\ncount()\tThe count() method for a tuple is used to count how many times a specified element appears in the tuple.\tSyntax:\n1\ntuple.count(value)\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\n3\nfruits = (“apple”, “banana”, “apple”, “orange”)\nprint(fruits.count(“apple”)) #Counts the number of times apple is found in tuple.\n#Output: 2\n\nCopied!\n\nWrap Toggled!\nindex()\tThe index() method in a tuple is used to find the first occurrence of a specified value and returns its position (index). If the value is not found, it raises a ValueError.\tSyntax:\n1\ntuple.index(value) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\n3\nfruits = (“apple”, “banana”, “orange”,”apple”)\nprint(fruits.index(“apple”)) #Returns the index value at which apple is present.\n#Output: 0\n\nCopied!\n\nWrap Toggled!\nsum()\tThe sum() function in Python can be used to calculate the sum of all elements in a tuple, provided that the elements are numeric (integers or floats).\tSyntax:\n1\nsum(tuple) \n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\n3\nnumbers = (10, 20, 5, 30)\nprint(sum(numbers))\n#Output: 65\n\nCopied!\n\nWrap Toggled!\nmin() and max()\tFind the smallest (min()) or largest (max()) element in a tuple.\tExample:\n1\n2\n3\n4\n5\nnumbers = (10, 20, 5, 30)\nprint(min(numbers)) \n#Output: 5\nprint(max(numbers))\n#Output: 30\n\nCopied!\n\nWrap Toggled!\nlen()\tGet the number of elements in the tuple using len().\tSyntax:\n1\nlen(tuple)\n\nCopied!\n\nWrap Toggled!\nExample:\n\n1\n2\n3\nfruits = (“apple”, “banana”, “orange”)\nprint(len(fruits)) #Returns length of the tuple.\n#Output: 3\n\nCopied!\n\nWrap Toggled!\n\n\u00a9 IBM Corporation. All rights reserved.\n\n\n\n
<\/div>\n\n\n\nGlossary: Python Basics\nWelcome! This alphabetized glossary contains many of the terms you’ll find within this course. This comprehensive glossary also includes additional industry-recognized terms not used in course videos. These terms are important for you to recognize when working in the industry, participating in user groups, and participating in other certificate programs.\n\nTerm\tDefinition\nAI\tAI (artificial intelligence) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.\nApplication development\tApplication development, or app development, is the process of planning, designing, creating, testing, and deploying a software application to perform various business operations.\nArithmetic Operations\tArithmetic operations are the basic calculations we make in everyday life like addition, subtraction, multiplication and division. It is also called as algebraic operations or mathematical operations.\nArray of numbers\tSet of numbers or objects that follow a pattern presented as an arrangement of rows and columns to explain multiplication.\nAssignment operator in Python\tAssignment operator is a type of Binary operator that helps in modifying the variable to its left with the use of its value to the right. The symbol used for assignment operator is “=”.\nAsterisk\tSymbol “* ” used to perform various operations in Python.\nBackslash\tA backslash is an escape character used in Python strings to indicate that the character immediately following it should be treated in a special way, such as being treated as escaped character or raw string.\nBoolean\tDenoting a system of algebraic notation used to represent logical propositions by means of the binary digits 0 (false) and 1 (true).\nColon\tA colon is used to represent an indented block. It is also used to fetch data and index ranges or arrays.\nConcatenate\tLink (things) together in a chain or series.\nData engineering\tData engineers are responsible for turning raw data into information that an organization can understand and use. Their work involves blending, testing, and optimizing data from numerous sources.\nData science\tData Science is an interdisciplinary field that focuses on extracting knowledge from data sets which are typically huge in amount. The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization.\nData type\tData type refers to the type of value a variable has and what type of mathematical, relational or logical operations can be applied without causing an error.\nDouble quote\tSymbol \u201c \u201c used to represent strings in Python.\nEscape sequence\tAn escape sequence is two or more characters that often begin with an escape character that tell the computer to perform a function or command.\nExpression\tAn expression is a combination of operators and operands that is interpreted to produce some other value.\nFloat\tPython float () function is used to return a floating-point number from a number or a string representation of a numeric value.\nForward slash\tSymbol \u201c\/\u201c used to perform various operations in Python\nFoundational\tDenoting an underlying basis or principle; fundamental.\nImmutable\tImmutable Objects are of in-built datatypes like int, float, bool, string, Unicode, and tuple. In simple words, an immutable object can\u2019t be changed after it is created.\nInteger\tAn integer is the number zero (0), a positive natural number (1, 2, 3, and so on) or a negative integer with a minus sign (\u22121, \u22122, \u22123, and so on.)\nManipulate\tIs the process of modifying a string or creating a new string by making changes to existing strings.\nMathematical conventions\tA mathematical convention is a fact, name, notation, or usage which is generally agreed upon by mathematicians.\nMathematical expressions\tExpressions in math are mathematical statements that have a minimum of two terms containing numbers or variables, or both, connected by an operator in between.\nMathematical operations\tThe mathematical \u201coperation\u201d refers to calculating a value using operands and a math operator.\nNegative indexing\tAllows you to access elements of a sequence (such as a list, a string, or a tuple) from the end, using negative numbers as indexes.\nOperands\tThe quantity on which an operation is to be done.\nOperators in Python\tOperators are used to perform operations on variables and values.\nParentheses\tParentheses is used to call an object.\nReplicate\tTo make an exact copy of.\nSequence\tA sequence is formally defined as a function whose domain is an interval of integers.\nSingle quote\tSymbol \u2018 \u2018 used to represent strings in python.\nSlicing in Python\tSlicing is used to return a portion from defined list.\nSpecial characters\tA special character is one that is not considered a number or letter. Symbols, accent marks, and punctuation marks are considered special characters.\nStride value\tStride is the number of bytes from one row of pixels in memory to the next row of pixels in memory.\nStrings\tIn Python, Strings are arrays of bytes representing Unicode characters.\nSubstring\tA substring is a sequence of characters that are part of an original string.\nType casting\tThe process of converting one data type to another data type is called Typecasting or Type Coercion or Type Conversion.\nTypes in Python\tData types are the classification or categorization of data items. It represents the kind of value that tells what operations can be performed on a particular data.\nVariables\tVariables are containers for storing data values.\n","protected":false},"excerpt":{"rendered":"

Glossary: Python Data Structures Welcome! This alphabetized glossary contains many of the terms in this course. This comprehensive glossary also includes additional industry-recognized terms not used in course videos. These terms are important for you to recognize when working in the industry, participating in user groups, and participating in other certificate programs. Term Definition Aliasing […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-3586","page","type-page","status-publish","hentry"],"yoast_head":"\nGlossary Python_Data Structures - fse-staging<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/potentsky.com\/fse-staging\/3586-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Glossary Python_Data Structures - fse-staging\" \/>\n<meta property=\"og:description\" content=\"Glossary: Python Data Structures Welcome! This alphabetized glossary contains many of the terms in this course. This comprehensive glossary also includes additional industry-recognized terms not used in course videos. These terms are important for you to recognize when working in the industry, participating in user groups, and participating in other certificate programs. 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