{"id":7721,"date":"2018-02-10T06:59:04","date_gmt":"2018-02-10T01:29:04","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=7721"},"modified":"2026-04-24T15:20:02","modified_gmt":"2026-04-24T09:50:02","slug":"python-collections-module","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/python-collections-module\/","title":{"rendered":"Python Collections Module – Python Counter, DefaultDict, OrderedDict, NamedTuple"},"content":{"rendered":"

Over the last few days, we opened ourselves up to three subclasses of the class \u2018dict\u2019, and the function namedtuple().<\/p>\n

In this Python Collections Module tutorial, we will study Python Counter, Python DefaultDict, Python OrderedDict, and Python NamedTuple with their subtypes, syntax, and examples.<\/p>\n

So, let’s start Python Collection Modules.<\/p>\n

\"Python<\/a>

Python Collections Module – Introduction<\/p><\/div>\n

What is the Python Collections Module?<\/h3>\n

The \u2018collections\u2019 module in Python that implements special container datatypes. These provide alternatives to Python\u2019s general-purpose built-in containers.<\/span><\/p>\n

As we said, all three of these are subclasses of the Python class \u2018dict\u2019.<\/p>\n

>>> from collections import Counter,defaultdict,OrderedDict,namedtuple\r\n>>> issubclass(Counter,dict) and issubclass(defaultdict,dict) and issubclass(OrderedDict,dict)<\/pre>\n
Output<\/strong><\/p>\n
True<\/div>\n
>>> type(namedtuple)<\/pre>\n
Output<\/strong><\/p>\n
<class ‘function’><\/div>\n
To use any of its functionality, we must first import it.<\/p>\n
>>> import collections<\/pre>\n
\"Python<\/a>
Python Collections Module<\/p><\/div>\n
Let’s see all the collections of Python<\/p>\n
Python Collections Counter<\/h3>\n
Python counter is a container that keeps count of the number of occurrences of any value in the container. It counts hashable objects.<\/p>\n
Features of the collection counter in Python:<\/strong><\/p>\n
    \n
  • Missing Keys:<\/strong> It is not like a standard dictionary that raises a keyerror; counter returns 0 for any missing elements.<\/li>\n
  • Negative count:<\/strong> The count can be of any value, anything from zero to negative.<\/li>\n
  • Initialization:<\/strong> You can initialize the count from a list or string, a dictionary, or a keyword argument.<\/li>\n<\/ul>\n
    Let\u2019s take an example.<\/p>\n
    1. Python Counter – Syntax<\/h4>\n
    To define a Python counter, we use the Counter() factory function. To it, we can pass a container like Python list or a tuple, or even a string or a dictionary. We may also use keyword arguments.<\/p>\n
    >>> from collections import Counter\r\n>>> c=Counter({'a':3,'b':2,'c':1})\r\n>>> c<\/pre>\n
    Output<\/strong><\/p>\n
    Counter({‘a’: 3, ‘b’: 2, ‘c’: 1})<\/div>\n
    >>> c=Counter('Hello')\r\n>>> c<\/pre>\n
    Output<\/strong><\/p>\n
    Counter({‘l’: 2, ‘H’: 1, ‘e’: 1, ‘o’: 1})<\/div>\n
    >>> c=Counter(a=3,b=2,c=1)\r\n>>> c<\/pre>\n
    Output<\/strong><\/p>\n
    Counter({‘a’: 3, ‘b’: 2, ‘c’: 1})<\/div>\n
    2. Updating a Python Counter<\/h4>\n
    To declare an empty counter in Python and then populate it, we use the update() method.<\/p>\n
    >>> c=Counter()\r\n>>> c.update('bfg')\r\n>>> c<\/pre>\n
    Output<\/strong><\/p>\n
    Counter({‘b’: 1, ‘f’: 1, ‘g’: 1})<\/div>\n
    3. Accessing Counts in Python<\/h4>\n
    To get the count of a value, we pass it as an index to the counter we defined.<\/p>\n
    >>> c['f']<\/pre>\n
    Output<\/strong><\/p>\n
    1<\/div>\n
    >>> c['h']<\/pre>\n
    As you can see, it does not raise a KeyError.<\/p>\n
    What are Python Errors and How to Handle Python Errors?<\/p>\n
    The elements() method returns a Python iterator for the values in the container.<\/p>\n
    >>> for i in c.elements():\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 print(f\"{i}: {c[i]}\")<\/pre>\n
    Output<\/strong><\/p>\n
    \n
    b: 1f: 1<\/p>\n
    g: 1<\/p>\n<\/div>\n
    We can also call the most_common() method to get the n most common values. These are the ones with the highest frequencies.<\/p>\n
    >>> c=Counter('hello')\r\n>>> c<\/pre>\n
    Output<\/strong><\/p>\n
    Counter({‘l’: 2, ‘h’: 1, ‘e’: 1, ‘o’: 1})<\/div>\n
    >>> c.most_common(2)<\/pre>\n
    Output<\/strong><\/p>\n
    [(‘l’, 2), (‘h’, 1)]<\/div>\n
    4. Python Counter Arithmetic<\/h4>\n
    We can also perform arithmetic on Python counters.<\/p>\n
    >>> c1=Counter('hello')\r\n>>> c2=Counter('help')\r\n>>> c1+c2<\/pre>\n
    Output<\/strong><\/p>\n
    Counter({‘l’: 3, ‘h’: 2, ‘e’: 2, ‘o’: 1, ‘p’: 1})<\/div>\n
    >>> c1&c2<\/pre>\n
    Output<\/strong><\/p>\n
    Counter({‘h’: 1, ‘e’: 1, ‘l’: 1})<\/div>\n
    Python DefaultDict<\/h3>\n
    Python DefaultDict collection lets us provide a default value for keys. We define it using the defaultdict() factory function, which takes another function as an argument. This function returns a default value for it.<\/p>\n
    1. Python DefaultDict – Syntax<\/h4>\n
    To define\u00a0Python defaultdict, we use the factory function defaultdict().<\/p>\n
    >>> from collections import defaultdict\r\n>>> d=defaultdict(lambda :35)\r\n>>> d['Ayushi']=95\r\n>>> d['Bree']=89\r\n>>> d['Leo']=90.5\r\n>>> d['Adam']<\/pre>\n
    Output<\/strong><\/p>\n
    35<\/div>\n
    Here, we did not initialize \u2018Adam\u2019. So, it took 35, because that\u2019s what our function returns to defaultdict(). We can also check the default value with the __missing__() method.<\/p>\n
    >>> d.__missing__('Adam')<\/pre>\n
    Output<\/strong><\/p>\n
    35<\/div>\n
    2. Using a Type as a Default Factory<\/h4>\n
    We can tell the interpreter what type of values we\u2019re going to work with. We do this by passing it as an argument to defaultdict().<\/p>\n
    >>> d=defaultdict(list)\r\n>>> for i,j in [('a',(1,2)),('b',(3,4)),('c',(5,6))]:\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 d[i].append(j)\r\n>>> d<\/pre>\n
    Output<\/strong><\/p>\n
    defaultdict(<class ‘list’>, {‘a’: [(1, 2)], ‘b’: [(3, 4)], ‘c’: [(5, 6)]})<\/div>\n
    Python OrderedDict<\/h3>\n
    Python OrderedDict remembers the order in which the key-value pairs were added. Let\u2019s take the Python ordereddict example.<\/p>\n
    >>> from collections import OrderedDict\r\n>>> o=OrderedDict()\r\n>>> o['a']=3\r\n>>> o['c']=1\r\n>>> o['b']=4\r\n>>> o<\/pre>\n
    Output<\/strong><\/p>\n
    OrderedDict([(‘a’, 3), (‘c’, 1), (‘b’, 4)])<\/div>\n
    1. Move_to_end() methods in Python<\/h4>\n
    We\u2019ll take a look at two methods in Python, orderedDict. The first we discuss is move_to_end(). It lets us move a key-value pair either to the end or to the front.<\/p>\n
    >>> o.move_to_end('c')\r\n>>> o<\/pre>\n
    Output<\/strong><\/p>\n
    OrderedDict([(‘a’, 3), (‘b’, 4), (‘c’, 1)])<\/div>\n
    >>> o.move_to_end('c',last=False)\r\n>>> o<\/pre>\n
    Output<\/strong><\/p>\n
    OrderedDict([(‘c’, 1), (‘a’, 3), (‘b’, 4)])<\/div>\n
    2. Popitem() methods in Python<\/h4>\n
    This method lets us pop a key-value pair out of the container, and then displays it.<\/p>\n
    >>> o.popitem()<\/pre>\n
    Output<\/strong><\/p>\n
    (‘b’, 4)<\/div>\n
    >>> o.popitem(last=False)<\/pre>\n
    Output<\/strong><\/p>\n
    (‘c’, 1)<\/div>\n
    >>> o<\/pre>\n
    Output<\/strong><\/p>\n
    OrderedDict([(‘a’, 3)])<\/div>\n
    Python NamedTuple<\/h3>\n
    Finally, in the Python collections module we discuss Python NamedTuple. This is a container that lets us access elements using names\/labels.<\/p>\n
    >>> from collections import namedtuple\r\n>>> colors=namedtuple('colors','r g b')\r\n>>> red=colors(r=255,g=0,b=0)<\/pre>\n
    1. Accessing Elements<\/h4>\n
    To access these elements, we use the dot Python operator.<\/p>\n
    >>> red.r<\/pre>\n
    Output<\/strong><\/p>\n
    255<\/div>\n
    >>> red.g<\/pre>\n
    >>> red.b<\/pre>\n
    Or, we could just use indices.<\/p>\n
    >>> red[0]<\/pre>\n
    Output<\/strong><\/p>\n
    255<\/div>\n
    We can also use the getattr() function.<\/p>\n
    >>> getattr(red,'r')<\/pre>\n
    Output<\/strong><\/p>\n
    255<\/div>\n
    Python namedtuple is immutable. So, you can\u2019t reassign a value directly.<\/p>\n
    >>> red.r=3\r\nTraceback (most recent call last):\r\nFile \"<pyshell#95>\", line 1, in <module>\r\nred.r=3<\/pre>\n
    Output<\/strong><\/p>\n
    AttributeError: can’t set attribute<\/div>\n
    2. Converting into a Python Dictionary<\/h4>\n
    To convert a namedtuple into a Python dictionary, we use the _asdict() method.<\/p>\n
    >>> red._asdict()<\/pre>\n
    Output<\/strong><\/p>\n
    OrderedDict([(‘r’, 255), (‘g’, 0), (‘b’, 0)])<\/div>\n
    3. Converting an Iterable into a namedtuple<\/h4>\n
    The _make() method lets us create a namedtuple from a Python list and the format we specified.<\/p>\n
    >>> colors._make(['1','2','3'])<\/pre>\n
    Output<\/strong><\/p>\n
    colors(r=’1′, g=’2′, b=’3′)<\/div>\n
    4. Creating a namedtuple from the dictionary<\/h4>\n
    To use a dictionary to make a Python namedtuple, we use this code:<\/p>\n
    >>> colors(**{'r':255,'g':0,'b':0})<\/pre>\n
    Output<\/strong><\/p>\n
    colors(r=255, g=0, b=0)<\/div>\n
    5. Checking What Fields Belong to the Tuple<\/h4>\n
    For this, we have the _fields attribute.<\/p>\n
    >>> red._fields<\/pre>\n
    Output<\/strong><\/p>\n
    (‘r’, ‘g’, ‘b’)<\/div>\n
    6. Changing a Value<\/h4>\n
    Like we said, Python namedtuples are immutable. But to change a value, we can use the _replace() method.<\/p>\n
    >>> red._replace(g=3)<\/pre>\n
    Output<\/strong><\/p>\n
    colors(r=255, g=3, b=0)<\/div>\n
    So, this was all about Python’s Collections Module Tutorial. Hope you like our explanation.<\/p>\n
    Python Interview Questions on Collection Modules<\/h3>\n
      \n
    1. What is the collection module in Python?<\/li>\n
    2. What are collection types in Python?<\/li>\n
    3. How do you use a collection counter in Python?<\/li>\n
    4. What are the collection data types in Python?<\/li>\n
    5. Is a string a collection in Python?<\/li>\n<\/ol>\n
      Conclusion<\/h3>\n
      Now that we\u2019ve discussed all four Python collections modules- Counter, defaultdict, OrderedDict, and namedtuple with their syntax, methods, and examples of Python collections modules.<\/p>\n","protected":false},"excerpt":{"rendered":"
      Over the last few days, we opened ourselves up to three subclasses of the class \u2018dict\u2019, and the function namedtuple(). In this Python Collections Module tutorial, we will study Python Counter, Python DefaultDict, Python...<\/p>\n","protected":false},"author":5,"featured_media":42238,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[46],"tags":[2637,10428,10429,10431,10485,10709,10710,10747],"class_list":["post-7721","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-collections-module-in-python","tag-python-collection-modules","tag-python-collections","tag-python-collections-tutorial","tag-python-defaultdict","tag-python-namedtuple","tag-python-namedtuple-accessing-elements","tag-python-ordereddict"],"yoast_head":"\nPython Collections Module - Python Counter, DefaultDict, OrderedDict, NamedTuple - DataFlair<\/title>\n<meta name=\"description\" content=\"Python collections module - Collections in Python, Collections Counter, Python defaultdict, Python ordereddict, Python namedtuple with syntax & 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:\/\/data-flair.training\/blogs\/python-collections-module\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Collections Module - 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