{"id":2644,"date":"2026-10-19T10:23:53","date_gmt":"2026-10-19T10:23:53","guid":{"rendered":"https:\/\/prwatech.in\/blog\/?p=2644"},"modified":"2026-10-20T05:27:05","modified_gmt":"2026-10-20T05:27:05","slug":"sets-in-python-tutorial","status":"publish","type":"post","link":"https:\/\/prwatech.in\/blog\/python\/python-data-types\/sets-in-python-tutorial\/","title":{"rendered":"Python Sets"},"content":{"rendered":"

Sets in Python Tutorial<\/b><\/strong><\/h1>\n

Imagine you collect the email addresses of everyone who signs up for an event. Some people sign up twice, and some use the same address at different times. You want each address exactly once. Or imagine two lists of students, one for the maths club and one for the science club, and you want to know who belongs to both. A Python set is made for exactly these jobs.<\/p>\n

A set is a collection of unique items. It throws away duplicates for you, it can tell you in an instant whether something is inside it, and it supports the operations you remember from school maths: union, intersection and difference. Sets are small, fast and very handy for cleaning data and comparing groups.<\/p>\n

In this tutorial, we explain what a set is and how to create one, including the empty set trap that catches almost every beginner. We cover how to add and remove items, with the difference between remove and discard. Then we look at the set operations: union, intersection, difference, and the subset and superset checks, with all the directions shown. We end with removing duplicates, frozen sets, common mistakes and the full program. Every output comes from a real run on Python 3.13.<\/p>\n

What are Python Sets?<\/h2>\n

A set is an unordered collection of unique items written in curly brackets.<\/p>\n

    \n
  • Unique.<\/strong> A value can appear only once. Adding a duplicate does nothing.<\/li>\n
  • Unordered.<\/strong> Items have no position. You cannot ask for the first one.<\/li>\n
  • Mutable.<\/strong> You can add and remove items. A set can change, but the items inside it must be unchangeable.<\/li>\n
  • Fast to search.<\/strong> Checking whether a value is in a set is much quicker than searching a list.<\/li>\n<\/ul>\n

    Creating a Set<\/h2>\n
    \n
    python<\/div>\n
    x = {1, 2, 4, 45}\r\nprint(x)\r\nprint({3, 1, 3, 2, 1})\r\nprint(set([5, 5, 6, 7]))\r\nprint(sorted(set(\"hello\")))<\/pre>\n
    output<\/div>\n
    {1, 2, 4, 45}\r\n{1, 2, 3}\r\n{5, 6, 7}\r\n['e', 'h', 'l', 'o']<\/pre>\n<\/div>\n
      \n
    • The second line had five numbers, but the set holds only three, because the repeats were dropped.<\/li>\n
    • set()<\/code> turns another collection, such as a list, into a set.<\/li>\n
    • A string passed to set<\/code> splits into single characters, and the repeated l in hello appears once. We used sorted<\/code> here because the order of text items in a set can change from run to run.<\/li>\n<\/ul>\n
      The Empty Set Trap<\/h3>\n
      Empty curly brackets make a dictionary, not a set. To get an empty set, use set()<\/code>.<\/p>\n
      \n
      python<\/div>\n
      empty_wrong = {}\r\nempty_right = set()\r\nprint(type(empty_wrong), type(empty_right))<\/pre>\n
      output<\/div>\n
      <class 'dict'> <class 'set'><\/pre>\n<\/div>\n
      No Order, No Indexing<\/h3>\n
      Since a set has no order, you cannot get an item by position. Small whole numbers often print in sorted order, but never rely on that.<\/p>\n
      \n
      python<\/div>\n
      x = {1, 2, 4, 45}\r\ntry:\r\n    x[0]\r\nexcept TypeError as error:\r\n    print(\"TypeError:\", error)<\/pre>\n
      output<\/div>\n
      TypeError: 'set' object is not subscriptable<\/pre>\n<\/div>\n
      Looping and Checking Membership<\/h3>\n
      A for<\/code> loop visits every item. The in<\/code> word checks whether a value is present.<\/p>\n
      \n
      python<\/div>\n
      x = {1, 2, 3, 4, 5, 6}\r\nfor z in x:\r\n    print(z, end=\" \")\r\nprint()\r\nprint(1 in x, 9 in x, 9 not in x)<\/pre>\n
      output<\/div>\n
      1 2 3 4 5 6 \r\nTrue False True<\/pre>\n<\/div>\n
      Methods for Sets in Python<\/h2>\n
      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      Set methods at a glance<\/caption>\n
      Method<\/th>\nWhat it does<\/th>\nReturns<\/th>\n<\/tr>\n<\/thead>\n
      add(item)<\/th>\nAdds one item<\/td>\nNone<\/td>\n<\/tr>\n
      update(iterable)<\/th>\nAdds many items at once<\/td>\nNone<\/td>\n<\/tr>\n
      remove(item)<\/th>\nRemoves an item, error if missing<\/td>\nNone<\/td>\n<\/tr>\n
      discard(item)<\/th>\nRemoves an item, no error if missing<\/td>\nNone<\/td>\n<\/tr>\n
      pop()<\/th>\nRemoves and returns an arbitrary item<\/td>\nThe item<\/td>\n<\/tr>\n
      clear()<\/th>\nRemoves every item<\/td>\nNone<\/td>\n<\/tr>\n
      copy()<\/th>\nMakes a copy<\/td>\nA new set<\/td>\n<\/tr>\n
      union(other)<\/th>\nAll items from both<\/td>\nA new set<\/td>\n<\/tr>\n
      intersection(other)<\/th>\nItems found in both<\/td>\nA new set<\/td>\n<\/tr>\n
      difference(other)<\/th>\nItems in the first but not the second<\/td>\nA new set<\/td>\n<\/tr>\n
      symmetric_difference(other)<\/th>\nItems in one but not both<\/td>\nA new set<\/td>\n<\/tr>\n
      issubset(other)<\/th>\nIs every item also in the other set?<\/td>\nTrue or False<\/td>\n<\/tr>\n
      issuperset(other)<\/th>\nDoes it contain every item of the other?<\/td>\nTrue or False<\/td>\n<\/tr>\n
      isdisjoint(other)<\/th>\nDo the two share nothing?<\/td>\nTrue or False<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
      add()<\/h3>\n
      add<\/code> puts in one item. Adding something that is already there changes nothing.<\/p>\n
      \n
      python<\/div>\n
      x = {1, 2, 3, 4, 5, 6}\r\nx.add(9)\r\nprint(x)\r\nx.add(9)\r\nprint(len(x))<\/pre>\n
      output<\/div>\n
      {1, 2, 3, 4, 5, 6, 9}\r\n7<\/pre>\n<\/div>\n
      update()<\/h3>\n
      update<\/code> adds many items from an iterable, such as a list, a tuple, or another set. Put the numbers in a list. Separate numbers, as in x.update(8, 11)<\/code>, fail.<\/p>\n
      \n
      python<\/div>\n
      x = {1, 2, 3, 4, 5, 6, 9}\r\nx.update([8, 11, 44, 22])\r\nprint(x)\r\nx.update([100], {200, 300})\r\nprint(sorted(x))\r\ntry:\r\n    x.update(8, 11)\r\nexcept TypeError as error:\r\n    print(\"TypeError:\", error)<\/pre>\n
      output<\/div>\n
      {1, 2, 3, 4, 5, 6, 8, 9, 11, 22, 44}\r\n[1, 2, 3, 4, 5, 6, 8, 9, 11, 22, 44, 100, 200, 300]\r\nTypeError: 'int' object is not iterable<\/pre>\n<\/div>\n
      You can pass more than one iterable, as the second line shows.<\/p>\n
      len()<\/h3>\n
      len<\/code> counts the items. It is a built in function, not a set method.<\/p>\n
      \n
      python<\/div>\n
      print(len({1, 2, 3}))<\/pre>\n
      output<\/div>\n
      3<\/pre>\n<\/div>\n
      remove() and discard()<\/h3>\n
      Both take out an item. The difference is what happens when the item is missing. remove<\/code> raises a KeyError, and discard<\/code> stays silent.<\/p>\n
      \n
      python<\/div>\n
      x = {1, 2, 3, 4, 5, 6}\r\nx.remove(2)\r\nprint(x)\r\nx.discard(99)\r\ntry:\r\n    x.remove(99)\r\nexcept KeyError as error:\r\n    print(\"KeyError:\", error)<\/pre>\n
      output<\/div>\n
      {1, 3, 4, 5, 6}\r\nKeyError: 99<\/pre>\n<\/div>\n
      The discard<\/code> call on 99 did nothing and raised no error. The remove<\/code> call on 99 raised the KeyError.<\/p>\n
      pop()<\/h3>\n
      pop<\/code> removes one item and hands it back. A set has no order, so it does not remove from the end. It removes an arbitrary item, and you cannot choose which one.<\/p>\n
      \n
      python<\/div>\n
      x = {1, 3, 4, 5, 6}\r\npopped = x.pop()\r\nprint(popped in {1, 3, 4, 5, 6}, len(x))<\/pre>\n
      output<\/div>\n
      True 4<\/pre>\n<\/div>\n
      clear()<\/h3>\n
      \n
      python<\/div>\n
      x = {1, 3, 4, 5}\r\nx.clear()\r\nprint(x, len(x))<\/pre>\n
      output<\/div>\n
      set() 0<\/pre>\n<\/div>\n
      An empty set prints as set()<\/code>, not as {}<\/code>, to avoid mixing it up with an empty dictionary.<\/p>\n
      del<\/h3>\n
      There is no x.delete()<\/code> method. del<\/code> is a statement, not a method. It removes the name itself, so using the set afterwards fails.<\/p>\n
      \n
      python<\/div>\n
      gone = {1, 2}\r\ndel gone\r\ntry:\r\n    print(gone)\r\nexcept NameError as error:\r\n    print(\"NameError:\", error)<\/pre>\n
      output<\/div>\n
      NameError: name 'gone' is not defined<\/pre>\n<\/div>\n
      copy()<\/h3>\n
      \n
      python<\/div>\n
      a = {1, 2, 3}\r\nb = a.copy()\r\nb.add(4)\r\nprint(a, b)<\/pre>\n
      output<\/div>\n
      {1, 2, 3} {1, 2, 3, 4}<\/pre>\n<\/div>\n
      The copy is independent, so adding to b<\/code> did not touch a<\/code>.<\/p>\n
      Set Operations<\/h2>\n
      Take two sets, x<\/code> and y<\/code>. Each operation has a method and a shortcut operator.<\/p>\n
      \n
      python<\/div>\n
      x = {1, 2, 3, 4, 5, 6}\r\ny = {4, 5, 6, 7, 8}\r\nprint(x.union(y), x | y)\r\nprint(x.intersection(y), x & y)\r\nprint(x.difference(y), x - y)\r\nprint(x.symmetric_difference(y), x ^ y)<\/pre>\n
      output<\/div>\n
      {1, 2, 3, 4, 5, 6, 7, 8} {1, 2, 3, 4, 5, 6, 7, 8}\r\n{4, 5, 6} {4, 5, 6}\r\n{1, 2, 3} {1, 2, 3}\r\n{1, 2, 3, 7, 8} {1, 2, 3, 7, 8}<\/pre>\n<\/div>\n
      \n\n\n\n\n\n\n\n\n
      Set operators<\/caption>\n
      Operation<\/th>\nOperator<\/th>\nIn plain words<\/th>\n<\/tr>\n<\/thead>\n
      union<\/th>\n|<\/td>\nEverything in either set<\/td>\n<\/tr>\n
      intersection<\/th>\n&<\/td>\nOnly what both sets have<\/td>\n<\/tr>\n
      difference<\/th>\n–<\/td>\nIn the first set but not in the second<\/td>\n<\/tr>\n
      symmetric difference<\/th>\n^<\/td>\nIn one set but not in both<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
      Think of the maths club and the science club. The union is everyone in either club. The intersection is the students who joined both. The difference is the students in maths who are not in science.<\/p>\n
      issubset() and issuperset()<\/h3>\n
      A set is a subset of another if every one of its items is also in the other. The other set is then a superset of it. The direction matters. Here x<\/code> has 6 items and small<\/code> has 3 of them:<\/p>\n
      \n
      python<\/div>\n
      x = {1, 2, 3, 4, 5, 6}\r\nsmall = {4, 5, 6}\r\nprint(x.issubset(small))\r\nprint(small.issubset(x))\r\nprint(x.issuperset(small))\r\nprint(small.issuperset(x))<\/pre>\n
      output<\/div>\n
      False\r\nTrue\r\nTrue\r\nFalse<\/pre>\n<\/div>\n
      \n\n\n\n\n\n\n\n\n
      Subset and superset checks<\/caption>\n
      Question<\/th>\nAnswer<\/th>\nWhy<\/th>\n<\/tr>\n<\/thead>\n
      Is x a subset of small?<\/th>\nFalse<\/td>\nx has items that small lacks<\/td>\n<\/tr>\n
      Is small a subset of x?<\/th>\nTrue<\/td>\nEvery item of small is in x<\/td>\n<\/tr>\n
      Is x a superset of small?<\/th>\nTrue<\/td>\nx contains all of small<\/td>\n<\/tr>\n
      Is small a superset of x?<\/th>\nFalse<\/td>\nsmall is missing items of x<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
      isdisjoint()<\/h3>\n
      isdisjoint<\/code> is True when two sets share no items.<\/p>\n
      \n
      python<\/div>\n
      print({1, 2}.isdisjoint({3, 4}), {1, 2}.isdisjoint({2, 3}))<\/pre>\n
      output<\/div>\n
      True False<\/pre>\n<\/div>\n
      Items in a Set Must Be Unchangeable<\/h2>\n
      A set can only hold hashable items, which in practice means items that cannot change. Numbers, text and tuples work. A list does not.<\/p>\n
      \n
      python<\/div>\n
      try:\r\n    bad = {[1, 2], 3}\r\nexcept TypeError as error:\r\n    print(\"TypeError:\", error)\r\nprint({(1, 2), 3} == {3, (1, 2)})<\/pre>\n
      output<\/div>\n
      TypeError: unhashable type: 'list'\r\nTrue<\/pre>\n<\/div>\n
      The last line shows a tuple inside a set, and that two sets with the same items are equal in any order.<\/p>\n
      Removing Duplicates With a Set<\/h2>\n
      This is the most common real use of a set. Turn a list into a set and back.<\/p>\n
      \n
      python<\/div>\n
      names = [\"Asha\", \"Ravi\", \"Asha\", \"Meena\", \"Ravi\"]\r\nprint(sorted(set(names)))\r\nprint(list(dict.fromkeys(names)))<\/pre>\n
      output<\/div>\n
      ['Asha', 'Meena', 'Ravi']\r\n['Asha', 'Ravi', 'Meena']<\/pre>\n<\/div>\n
      The first line removes the duplicates but loses the original order, so the output is sorted here. If order matters, use the second form. A dictionary remembers the order of first appearance, and it gave Asha, Ravi, Meena, the order they came in.<\/p>\n
      Set Comprehension and Frozen Sets<\/h2>\n
      \n
      python<\/div>\n
      print({n % 3 for n in range(10)})<\/pre>\n
      output<\/div>\n
      {0, 1, 2}<\/pre>\n<\/div>\n
      A set comprehension builds a set in one line. Ten numbers gave only three remainders, because the repeats collapsed.<\/p>\n
      A frozenset is a set that cannot be changed. Because it is unchangeable, it can be used as a dictionary key or placed inside another set.<\/p>\n
      \n
      python<\/div>\n
      fs = frozenset([1, 2, 3])\r\ntry:\r\n    fs.add(4)\r\nexcept AttributeError as error:\r\n    print(\"AttributeError:\", error)<\/pre>\n
      output<\/div>\n
      AttributeError: 'frozenset' object has no attribute 'add'<\/pre>\n<\/div>\n
      Set Compared With List, Tuple and Dictionary<\/h2>\n
      \n\n\n\n\n\n\n\n\n
      Four collection types compared<\/caption>\n
      Type<\/th>\nWritten as<\/th>\nOrdered<\/th>\nDuplicates<\/th>\nBest for<\/th>\n<\/tr>\n<\/thead>\n
      List<\/th>\n[1, 2, 3]<\/td>\nYes<\/td>\nAllowed<\/td>\nA collection that grows or changes<\/td>\n<\/tr>\n
      Tuple<\/th>\n(1, 2, 3)<\/td>\nYes<\/td>\nAllowed<\/td>\nA fixed group of values<\/td>\n<\/tr>\n
      Set<\/th>\n{1, 2, 3}<\/td>\nNo<\/td>\nNot allowed<\/td>\nUnique items and group comparisons<\/td>\n<\/tr>\n
      Dictionary<\/th>\n{“a”: 1}<\/td>\nYes<\/td>\nKeys must be unique<\/td>\nLooking up values by key<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
      Common Mistakes to Avoid<\/h2>\n
        \n
      • Using empty curly brackets for an empty set.<\/strong> That makes a dictionary. Use set()<\/code>.<\/li>\n
      • Expecting order.<\/strong> A set has no order and no index.<\/li>\n
      • Passing separate numbers to update.<\/strong> Pass one iterable, such as a list.<\/li>\n
      • Using remove for an item that may be missing.<\/strong> It raises a KeyError. Use discard<\/code>.<\/li>\n
      • Getting the direction of subset and superset wrong.<\/strong> A.issubset(B)<\/code> asks whether A fits inside B.<\/li>\n
      • Putting a list inside a set.<\/strong> Lists cannot be hashed. Use a tuple.<\/li>\n
      • Using a set to remove duplicates when order matters.<\/strong> Use dict.fromkeys<\/code> instead.<\/li>\n
      • Writing set.delete().<\/strong> It does not exist. Use del<\/code>, or clear<\/code> to empty it.<\/li>\n<\/ul>\n
        Try It Yourself: The Full Program<\/h2>\n
        The program below holds every example in this tutorial. Copy it into a file, run it, and compare your output with what you see above.<\/p>\n
        \n
        python<\/div>\n
        import sys\r\nprint(\"Python\", sys.version_info.major, sys.version_info.minor)\r\n\r\n# Creating sets\r\nx = {1, 2, 4, 45}\r\nprint(x)\r\nprint({3, 1, 3, 2, 1})\r\nprint(set([5, 5, 6, 7]))\r\nprint(sorted(set(\"hello\")))\r\nempty_wrong = {}\r\nempty_right = set()\r\nprint(type(empty_wrong), type(empty_right))\r\n\r\n# No order and no indexing\r\ntry:\r\n    x[0]\r\nexcept TypeError as error:\r\n    print(\"TypeError:\", error)\r\n\r\n# Looping and membership\r\nx = {1, 2, 3, 4, 5, 6}\r\nfor z in x:\r\n    print(z, end=\" \")\r\nprint()\r\nprint(1 in x, 9 in x, 9 not in x)\r\n\r\n# Adding\r\nx.add(9)\r\nprint(x)\r\nx.add(9)\r\nprint(len(x))\r\nx.update([8, 11, 44, 22])\r\nprint(x)\r\nx.update([100], {200, 300})\r\nprint(sorted(x))\r\ntry:\r\n    x.update(8, 11)\r\nexcept TypeError as error:\r\n    print(\"TypeError:\", error)\r\n\r\n# Removing\r\nx = {1, 2, 3, 4, 5, 6}\r\nx.remove(2)\r\nprint(x)\r\nx.discard(99)\r\ntry:\r\n    x.remove(99)\r\nexcept KeyError as error:\r\n    print(\"KeyError:\", error)\r\npopped = x.pop()\r\nprint(popped in {1, 3, 4, 5, 6}, len(x))\r\nx.clear()\r\nprint(x, len(x))\r\ngone = {1, 2}\r\ndel gone\r\ntry:\r\n    print(gone)\r\nexcept NameError as error:\r\n    print(\"NameError:\", error)\r\n\r\n# Copy\r\na = {1, 2, 3}\r\nb = a.copy()\r\nb.add(4)\r\nprint(a, b)\r\n\r\n# Set operations\r\nx = {1, 2, 3, 4, 5, 6}\r\ny = {4, 5, 6, 7, 8}\r\nprint(x.union(y), x | y)\r\nprint(x.intersection(y), x & y)\r\nprint(x.difference(y), x - y)\r\nprint(x.symmetric_difference(y), x ^ y)\r\n\r\n# Subset and superset\r\nsmall = {4, 5, 6}\r\nprint(x.issubset(small))\r\nprint(small.issubset(x))\r\nprint(x.issuperset(small))\r\nprint(small.issuperset(x))\r\nprint({1, 2}.isdisjoint({3, 4}), {1, 2}.isdisjoint({2, 3}))\r\n\r\n# Items must be unchangeable\r\ntry:\r\n    bad = {[1, 2], 3}\r\nexcept TypeError as error:\r\n    print(\"TypeError:\", error)\r\nprint({(1, 2), 3} == {3, (1, 2)})\r\n\r\n# Removing duplicates, and what it costs\r\nnames = [\"Asha\", \"Ravi\", \"Asha\", \"Meena\", \"Ravi\"]\r\nprint(sorted(set(names)))\r\nprint(list(dict.fromkeys(names)))\r\n\r\n# Set comprehension and frozenset\r\nprint({n % 3 for n in range(10)})\r\nfs = frozenset([1, 2, 3])\r\ntry:\r\n    fs.add(4)\r\nexcept AttributeError as error:\r\n    print(\"AttributeError:\", error)<\/pre>\n<\/div>\n
         <\/p>\n
        Sets do one job very well: they keep things unique and make group comparisons easy. Run the examples, change the numbers, and watch how union, intersection and difference behave. When they feel natural, you will reach for a set whenever you hear the words unique, duplicate or overlap. Then move on to the other Python data types to see how all five fit together.<\/p>\n","protected":false},"excerpt":{"rendered":"
        Sets in Python Tutorial Imagine you collect the email addresses of everyone who signs up for an event. Some people sign up twice, and some use the same address at different times. You want each address exactly once. Or imagine two lists of students, one for the maths club and one for the science club, […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[28,1671],"tags":[358],"class_list":["post-2644","post","type-post","status-publish","format-standard","hentry","category-python","category-python-data-types","tag-sets-in-python-tutorial"],"acf":[],"yoast_head":"\nPython Sets Tutorial for Beginners With Examples | Prwatech<\/title>\n<meta name=\"description\" content=\"Learn Python sets step by step: create them, add and remove items, use union, intersection and difference, and remove duplicates, with tested outputs.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/prwatech.in\/blog\/python\/python-data-types\/sets-in-python-tutorial\/\" 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