{"id":74692,"date":"2020-01-02T10:41:23","date_gmt":"2020-01-02T05:11:23","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=74692"},"modified":"2020-01-02T10:41:23","modified_gmt":"2020-01-02T05:11:23","slug":"python-data-structures","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/python-data-structures\/","title":{"rendered":"Python Data Structures – Learn to choose data structures wisely"},"content":{"rendered":"

Python is dynamically<\/strong> typed and you don\u2019t have to declare data type<\/strong> when declaring a variable<\/strong>. But Python has many data structures – collections of data<\/strong>, for different purposes.<\/p>\n

In this tutorial, we will discuss different types of Python data structures in brief.<\/p>\n

What is Python Data Structure?<\/h3>\n

Python has primary data types like Boolean<\/strong>, int<\/strong>, float<\/strong>, and complex<\/strong>. It also has data structures to store <\/strong>collections of values<\/strong>.<\/p>\n

The data structures in Python are lists<\/strong>, tuple<\/strong>, dictionaries<\/strong>, sets<\/strong>, and strings<\/strong>. Some of them are mutable<\/strong> and the others are immutable<\/strong>.<\/p>\n

    \n
  • Lists<\/li>\n
  • Tuples<\/li>\n
  • Sets and frozensets<\/li>\n
  • Dictionaries<\/li>\n
  • Strings<\/li>\n<\/ul>\n

    Let’s discuss the types of data structures in detail.<\/p>\n

    Types of Python Data Structures<\/h3>\n

    1. Lists in Python<\/h4>\n

    Python does not have arrays<\/strong> but it has lists<\/strong>. Lists are ordered collections of items<\/strong>, they are mutable<\/strong> and heterogeneous<\/strong>.<\/p>\n

    Let\u2019s take an example.<\/p>\n

    a. Creating List in Python<\/h5>\n
    >>> groceries=['milk','eggs']<\/pre>\n
    This list is called groceries<\/strong> and it has milk and eggs.<\/p>\n
    Let\u2019s see how we will access these values.<\/p>\n
    >>> groceries[0]<\/pre>\n
    Output:<\/strong><\/p>\n
    \u2018milk\u2019<\/div>\n
    >>> groceries[1]<\/pre>\n
    Output:<\/strong><\/p>\n
    \u2018eggs\u2019<\/div>\n
    We can also create lists with the list() function<\/strong>.<\/p>\n
    >>> nums=list()\n>>> nums.append(4)\n>>> nums1=[]\n>>> nums1.append(5)\n>>> nums2=list((1,2,3,4))<\/pre>\n
    b. Indexing in Python<\/h5>\n
    We access them by indexing. Indexing starts at 0<\/strong>. \u2018milk\u2019 has index 0<\/strong>, not 1<\/strong>. And \u2018eggs\u2019 has index 1<\/strong>, not 2<\/strong>.<\/p>\n
    Index 2 does not exist, so trying to access it gives us an IndexError<\/strong>.<\/p>\n
    >>> groceries[2]<\/pre>\n
    Output:<\/strong><\/p>\n
    Traceback (most recent call last):
    \n\u00a0\u00a0<\/b>File “<pyshell#3>”, line 1, in <module>
    \n\u00a0\u00a0\u00a0\u00a0<\/b>groceries[2]
    \nIndexError: list index out of range<\/div>\n
    >>> groceries[-1]<\/pre>\n
    Output:<\/strong><\/p>\n
    \u2018eggs\u2019<\/div>\n
    c. Length in Python<\/h5>\n
    This list has 2 items, milk, and eggs.<\/p>\n
    >>> len(groceries)<\/pre>\n
    Output:<\/strong><\/p>\n
    2<\/div>\n
    d. Adding items in Python<\/h5>\n
    We can append<\/strong> an item with the append() method<\/strong>.<\/p>\n
    >>> groceries.append('bacon')\n>>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘milk’, ‘eggs’, ‘bacon’]<\/div>\n
    We can also extend<\/strong> the list so that it holds more values.<\/p>\n
    >>> groceries.extend(['ham','bread'])\n>>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘milk’, ‘eggs’, ‘bacon’, ‘ham’, ‘bread’]<\/div>\n
    e. Checking membership<\/h4>\n
    We can check whether an item exists<\/strong> in the list. We use the \u2018in\u2019<\/strong> and \u2018not in\u2019<\/strong> operators.<\/p>\n
    >>> 'eggs' in groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    True<\/div>\n
    >>> 'banana' in groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    False<\/div>\n
    f. Slicing in Python<\/h5>\n
    If you only want a part<\/strong> of the list, you can slice<\/strong> it with []<\/strong>.<\/p>\n
    >>> groceries[1:4]<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘eggs’, ‘bacon’, ‘ham’]<\/div>\n
    >>> groceries[-3:-1]<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘bacon’, ‘ham’]<\/div>\n
    g. Modifying in Python<\/h5>\n
    Lists are mutable<\/strong> so we can change<\/strong> its items.<\/p>\n
    >>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘milk’, ‘eggs’, ‘bacon’, ‘ham’, ‘bread’]<\/div>\n
    >>> groceries[2]='cheese'\n>>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘milk’, ‘eggs’, ‘cheese’, ‘ham’, ‘bread’]<\/div>\n
    h. Deleting in Python<\/h5>\n
    You can use the del keyword<\/strong> to delete items in a list.<\/p>\n
    >>> del groceries[1]\n>>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘milk’, ‘cheese’, ‘ham’, ‘bread’]<\/div>\n
    >>> groceries.pop()<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘bread’<\/div>\n
    >>> groceries.remove('ham')\n>>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘milk’, ‘cheese’]<\/div>\n
    i. Looping in Python<\/h5>\n
    You can use a list in a for-loop<\/strong>.<\/p>\n
    for item in groceries:\n    print(item)<\/pre>\n
    Output:<\/strong><\/p>\n
    milk
    \ncheese<\/div>\n
    j. Concatenating in Python<\/h5>\n
    We can use the + operator<\/strong> to join two lists<\/strong>.<\/p>\n
    >>> [1,2,3]+[4,5]<\/pre>\n
    Output:<\/strong><\/p>\n
    [1, 2, 3, 4, 5]<\/div>\n
    You can also multiply<\/strong> it by an integer<\/strong> to repeat<\/strong> it.<\/p>\n
    >>> [1,2]*3<\/pre>\n
    Output:<\/strong><\/p>\n
    [1, 2, 1, 2, 1, 2]<\/div>\n
    k. Copying in Python<\/h5>\n
    copy()<\/strong> and list()<\/strong> create shallow copies<\/strong> of a list.<\/p>\n
    >>> groceries.copy()<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘milk’, ‘cheese’]<\/div>\n
    >>> list(groceries)<\/pre>\n
    Output:<\/strong><\/p>\n
    [‘milk’, ‘cheese’]<\/div>\n
    l. Nested lists and comprehension in Python<\/h5>\n
    You can make a list hold<\/strong> another list.<\/p>\n
    >>> nums=[1,2,[3,4],5]<\/pre>\n
    You can also define<\/strong> it quickly.<\/p>\n
    >>> nums=[num for num in range(7)]\n>>> nums<\/pre>\n
    Output:<\/strong><\/p>\n
    [0, 1, 2, 3, 4, 5, 6]<\/div>\n
    Python Tuples<\/h4>\n
    In python data structures, Tuples are like lists, ordered collections<\/strong> of items. They can be heterogeneous<\/strong> but they are immutable<\/strong>. This means you cannot change<\/strong> their values<\/strong>.<\/p>\n
    Let\u2019s take an example.<\/p>\n
    1. Creating tuples in Python<\/h5>\n
    >>> groceries=('milk','eggs')<\/pre>\n
    This tuple is called groceries<\/strong> and it has milk and eggs.<\/p>\n
    Let\u2019s see how we will access these values.<\/p>\n
    >>> groceries[0]<\/pre>\n
    Output:<\/strong><\/p>\n
    \u2018milk\u2019<\/div>\n
    >>> groceries[1]<\/pre>\n
    Output:<\/strong><\/p>\n
    \u2018eggs\u2019<\/div>\n
    We can also create tuples with the tuple() function<\/strong>.<\/p>\n
    >>> nums=tuple([1,2,3])\n>>> nums<\/pre>\n
    Output:<\/strong><\/p>\n
    (1, 2, 3)<\/div>\n
    a. Indexing in Python<\/h5>\n
    Like lists, we access them by indexing<\/strong>. Indexing starts at 0<\/strong>. \u2018milk\u2019 has index 0 and \u2018eggs\u2019 has index 1<\/strong>. Index 2 does not exist, so trying to access it gives us an IndexError<\/strong>.<\/p>\n
    >>> groceries[2]<\/pre>\n
    Output:<\/strong><\/p>\n
    Traceback (most recent call last):
    \n\u00a0\u00a0<\/b>File “<pyshell#3>”, line 1, in <module>
    \n\u00a0\u00a0\u00a0\u00a0<\/b>groceries[2]
    \nIndexError: tuple index out of range<\/div>\n
    >>> groceries[-1]<\/pre>\n
    Output:<\/strong><\/p>\n
    \u2018eggs\u2019<\/div>\n
    b. Length in Python<\/h5>\n
    This tuple<\/strong> has 2 items too, milk and eggs.<\/p>\n
    >>> len(groceries)<\/pre>\n
    Output:<\/strong><\/p>\n
    2<\/div>\n
    c. Adding items in Python<\/h5>\n
    Tuples are immutable<\/strong> so we cannot add items to them.<\/p>\n
    d. Checking membership in Python<\/h5>\n
    We can check whether an item exists<\/strong> in a tuple. We use the \u2018in\u2019<\/strong> and \u2018not in\u2019<\/strong> operators here as well.<\/p>\n
    >>> 'eggs' in groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    True<\/div>\n
    >>> 'banana' in groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    False<\/div>\n
    f. Slicing in Python<\/h5>\n
    If you only want a part<\/strong> of the list, you can slice<\/strong> it with []<\/strong>.<\/p>\n
    >>> groceries[1:4]<\/pre>\n
    Output:<\/strong><\/p>\n
    (‘eggs’,)<\/div>\n
    >>> groceries[:]<\/pre>\n
    Output:<\/strong><\/p>\n
    (‘milk’, ‘eggs’)<\/div>\n
    g. Modifying in Python<\/h5>\n
    Tuples are immutable<\/strong> so we cannot modify<\/strong> elements.<\/p>\n
    h. Deleting tuple in Python<\/h5>\n
    You can delete<\/strong> a complete tuple<\/strong> but you cannot delete<\/strong> elements<\/strong> from it.<\/p>\n
    >>> del groceries[1]<\/pre>\n
    Output:<\/strong><\/p>\n
    Traceback (most recent call last):
    \n\u00a0\u00a0<\/b>File “<pyshell#50>”, line 1, in <module>
    \n\u00a0\u00a0\u00a0\u00a0<\/b>del groceries[1]
    \nTypeError: ‘tuple’ object doesn’t support item deletion<\/div>\n
    i. Looping in Python<\/h5>\n
    You can use a tuple as well in a for-loop<\/strong>.<\/p>\n
    >>> for item in groceries:\n  print(item)<\/pre>\n
    Output:<\/strong><\/p>\n
    milk
    \ncheese<\/div>\n
    j. Concatenating tuples in Python<\/h5>\n
    We can use the + operator<\/strong> to join<\/strong> two tuples.<\/p>\n
    >>> (1,2,3)+(4,5)<\/pre>\n
    Output:<\/strong><\/p>\n
    (1, 2, 3, 4, 5)<\/div>\n
    You can also multiply<\/strong> it by an integer<\/strong> to repeat it like in lists.<\/p>\n
    >>> (1,2)*3<\/pre>\n
    Output:<\/strong><\/p>\n
    (1, 2, 1, 2, 1, 2)<\/div>\n
    k. Copying<\/h5>\n
    Tuples do not<\/strong> have the copy() method<\/strong>. You can use the tuple() function<\/strong> to create a tuple.<\/p>\n
    >>> tuple((1,2,3))<\/pre>\n
    Output:<\/strong><\/p>\n
    (1, 2, 3)<\/div>\n
    l. Nested tuples in Python<\/h5>\n
    Tuples can hold<\/strong> tuples.<\/p>\n
    >>> nums=(1,2,(3,4),5)<\/pre>\n
    But tuples don\u2019t have tuple comprehension<\/strong>.<\/p>\n
    Sets and frozensets in Python<\/h4>\n
    In Python data structures, sets are collections of values and are unordered<\/strong>, heterogeneous<\/strong>, not indexed<\/strong> and do not<\/strong> hold duplicate elements<\/strong>.<\/p>\n
    1. Creating<\/strong><\/h5>\n
    You can create a set with curly braces {}<\/strong> and separating elements with commas<\/strong>.<\/p>\n
    This is a set:<\/p>\n
    >>> myset={1, 2.0, False}\n\n>>> set([1, 2.0])<\/pre>\n
    Output:<\/strong><\/p>\n
    {1, 2.0}<\/div>\n
    You can also use the set() function<\/strong>.<\/p>\n
    2. Indexing<\/h5>\n
    You cannot<\/strong> index or slice sets.<\/p>\n
    3. Length<\/h5>\n
    You can get a set\u2019s length with the len() function<\/strong>.<\/p>\n
    >>> len(myset)<\/pre>\n
    Output:<\/strong><\/p>\n
    3<\/div>\n
    4. Adding items<\/h5>\n
    You can add items with the add() method<\/strong>.<\/p>\n
    >>> myset.add(6)\n>>> myset<\/pre>\n
    Output:<\/strong><\/p>\n
    {False, 1, 2.0, 6}<\/div>\n
    >>> myset.update([7,9])\n>>> myset<\/pre>\n
    Output:<\/strong><\/p>\n
    {False, 1, 2.0, 6, 7, 9}<\/div>\n
    5. Deleting items<\/h5>\n
    There are many methods<\/strong> for doing this.<\/p>\n
    >>> myset={1,2,3,4,5,5,6,7,8,9}\n>>> myset<\/pre>\n
    Output:<\/strong><\/p>\n
    {1, 2, 3, 4, 5, 6, 7, 8, 9}<\/div>\n
    >>> myset.discard(5)\n>>> myset<\/pre>\n
    Output:<\/strong><\/p>\n
    {1, 2, 3, 4, 6, 7, 8, 9}<\/div>\n
    >>> myset.remove(6)\n>>> myset<\/pre>\n
    Output:<\/strong><\/p>\n
    {1, 2, 3, 4, 7, 8, 9}<\/div>\n
    >>> myset.pop()<\/pre>\n
    Output:<\/strong><\/p>\n
    1<\/div>\n
    >>> myset.clear()\n>>> myset<\/pre>\n
    Output:<\/strong><\/p>\n
    set()<\/div>\n
    You can perform operations on sets like union<\/strong>, intersection<\/strong> and set-difference<\/strong>.<\/p>\n
    We will discuss them later.<\/p>\n
    frozenset in Python<\/h3>\n
    frozensets are immutable<\/strong> sets. They can be used as keys<\/strong> for dictionaries<\/strong>– regular sets can’t be used.<\/p>\n
    >>> frozenset((1,2,3))<\/pre>\n
    Output:<\/strong><\/p>\n
    frozenset({1, 2, 3})<\/div>\n
    >>> {'a':1, frozenset((1,2,3)):2}<\/pre>\n
    Output:<\/strong><\/p>\n
    {‘a’: 1, frozenset({1, 2, 3}): 2}<\/div>\n
    Python Dictionaries<\/h3>\n
    In python data structures, Dictionaries are unordered<\/strong> mutable<\/strong> and indexed<\/strong> collections<\/strong> of items.<\/p>\n
    Dictionaries have key-value<\/strong> pairs. They are created in curly<\/strong> braces. <\/strong>Dictionary keys are unique<\/strong> and immutable<\/strong>.<\/p>\n
    1. Creating<\/h4>\n
    You can create a dictionary<\/strong> with curly braces<\/strong> and separating keys<\/strong> and values<\/strong> by colons<\/strong> and key-value<\/strong> pairs by commas<\/strong>.<\/p>\n
    >>> groceries={'milk':25, 'eggs':15}<\/pre>\n
    You can also use the dict() function<\/strong> to create a dictionary.<\/p>\n
    >>> groceries=dict([(1,2),(3,4)])\n>>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    {1: 2, 3: 4}<\/div>\n
    2. Indexing<\/h4>\n
    You can index dictionaries with keys<\/strong>.<\/p>\n
    >>> groceries={'milk':25, 'eggs':15}\n>>> groceries['milk']<\/pre>\n
    Output:<\/strong><\/p>\n
    25<\/div>\n
    3. Accessing elements<\/h4>\n
    Other than index<\/strong>, you can access elements in this way:<\/p>\n
    >>> groceries.get('milk')<\/pre>\n
    Output:<\/strong><\/p>\n
    25<\/div>\n
    4. Adding items<\/h4>\n
    You can add key-value<\/strong> pairs to dictionaries<\/strong>.<\/p>\n
    >>> groceries['cheese']=30\n>>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    {‘milk’: 25, ‘eggs’: 15, ‘cheese’: 30}<\/div>\n
    5. Removing items<\/h4>\n
    We can remove<\/strong> items from dictionaries.<\/p>\n
    >>> groceries={'milk':25, 'eggs':15, 'cheese':20, 'bread':15}\n>>> groceries.pop('eggs')<\/pre>\n
    Output:<\/strong><\/p>\n
    15<\/div>\n
    >>> groceries.popitem()<\/pre>\n
    Output:<\/strong><\/p>\n
    (‘bread’, 15)<\/div>\n
    >>> del groceries['cheese']\n>>> groceries.clear()\n>>> groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    {}<\/div>\n
    6. Changing values<\/h4>\n
    We can change<\/strong> values in a dictionary.<\/p>\n
    >>> groceries={'milk':25, 'eggs':15, 'cheese':20, 'bread':15}\n>>> groceries['eggs']=30<\/pre>\n
    7. Looping<\/h4>\n
    We can loop<\/strong> on dictionaries with a for-loop<\/strong>.<\/p>\n
    for key,value in groceries.items():\n    print(key,value)<\/pre>\n
    Output:<\/strong><\/p>\n
    milk 25
    \neggs 30
    \ncheese 20
    \nbread 15<\/div>\n
    8. Length and membership<\/h4>\n
    We can get a dictionary\u2019s length<\/strong>.<\/p>\n
    >>> len(groceries)<\/pre>\n
    Output:<\/strong><\/p>\n
    4<\/div>\n
    And we can test if a key<\/strong> is in a dictionary.<\/p>\n
    >>> 'bread' in groceries<\/pre>\n
    Output:<\/strong><\/p>\n
    True<\/div>\n
    9. Copying<\/h4>\n
    We can create shallow copies<\/strong> of a dictionary.<\/p>\n
    >>> groceries.copy()<\/pre>\n
    Output:<\/strong><\/p>\n
    {‘milk’: 25, ‘eggs’: 30, ‘cheese’: 20, ‘bread’: 15}<\/div>\n
    >>> dict(groceries)<\/pre>\n
    Output:<\/strong><\/p>\n
    {‘milk’: 25, ‘eggs’: 30, ‘cheese’: 20, ‘bread’: 15}<\/div>\n
    10. Nested dictionaries and comprehension<\/h4>\n
    Dictionaries can contain dictionaries as values<\/strong> but not as keys<\/strong>.<\/p>\n
    >>> dict1={'a': {'a':1, 'b':2}, 'c':3}<\/pre>\n
    We can also create dictionaries by comprehension<\/strong>.<\/p>\n
    >>> {num:num**2 for num in range(6)}<\/pre>\n
    Output:<\/strong><\/p>\n
    {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}<\/div>\n
    Python Strings<\/h3>\n
    Python has strings<\/strong> – sequences of characters<\/strong>. We can declare them in single quotes<\/strong> or double-quotes<\/strong>.<\/p>\n
    >>> 'dog'<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘dog’<\/div>\n
    >>> \"dog\"<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘dog’<\/div>\n
    >>> 'dog\\'s gone bad'<\/pre>\n
    Output:<\/strong><\/p>\n
    “dog’s gone bad”<\/div>\n
    When we want to include quotes<\/strong> in a string surrounded by the same quotes, we can use escape characters<\/strong>.<\/p>\n
    1. Indexing<\/h4>\n
    We can index strings<\/strong> too.<\/p>\n
    1.1. Positive indexing:<\/h4>\n
    >>> text='I had a bad day today'\n>>> text[6]<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘a’<\/div>\n
    >>> text[-5]<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘t’<\/div>\n
    2. Slicing<\/h4>\n
    We can slice strings when we only want a part<\/strong> of them.<\/p>\n
    >>> text[2:9]<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘had a b’<\/div>\n
    >>> text[-9:-2]<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘day tod’<\/div>\n
    >>> text[2::3]<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘h b yoy’<\/div>\n
    3. Deleting<\/h4>\n
    We can delete complete strings<\/strong> but not a character or a slice.<\/p>\n
    >>> del text\n>>><\/pre>\n
    4. Concatenation and membership<\/h4>\n
    We can join<\/strong> two strings with the + operator<\/strong>.<\/p>\n
    >>> 'hello'+'world'<\/pre>\n
    Output:<\/strong><\/p>\n
    ‘helloworld’<\/div>\n
    You can check if a value<\/strong> is in a string<\/strong>.<\/p>\n
    >>> 'hell' in 'hello'<\/pre>\n
    Output:<\/strong><\/p>\n
    True<\/div>\n
    5. Length<\/h4>\n
    We can calculate the length of a string with the len() function<\/strong>.<\/p>\n
    >>> len('hello')<\/pre>\n
    Output:<\/strong><\/p>\n
    5<\/div>\n
    We can also format strings with the % operator<\/strong>, f-strings<\/strong> and the format() method<\/strong>.<\/p>\n
    Summary<\/h3>\n
    In TechVidvan’s Python data structures article, we learned about data structures<\/strong>, lists<\/strong>, tuples<\/strong>, sets<\/strong> and frozensets<\/strong>, dictionaries<\/strong> and strings<\/strong>. These are the various types of data structures in Python. We can use them to store collections<\/strong> of values<\/strong>.<\/p>\n
    This was all about python data structures.<\/p>\n","protected":false},"excerpt":{"rendered":"
    Python is dynamically typed and you don\u2019t have to declare data type when declaring a variable. But Python has many data structures – collections of data, for different purposes. In this tutorial, we will...<\/p>\n","protected":false},"author":1,"featured_media":75071,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1053],"tags":[1162,1163,1164,1165,1166,1167],"class_list":["post-74692","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-data-structures-in-python","tag-python-data-structures","tag-python-dictionaries","tag-python-tuple","tag-types-of-data-structures-in-python","tag-what-is-python-data-structure"],"yoast_head":"\nPython Data Structures - Learn to choose data structures wisely - TechVidvan<\/title>\n<meta name=\"description\" content=\"Python Data Structures - Get ready to explore data structure in Python & learn tuples, sets and frozensets, dictionaries, & strings in Python with examples.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/techvidvan.com\/tutorials\/python-data-structures\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Data Structures - 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