<\/span><\/h2>\n\n\n\nCreating a defaultdict is similar to creating a regular dictionary. We just need to import defaultdict first from the collections module and pass the default_factory parameter to its constructor.<\/p>\n\n\n\n
For example, let’s create a defaultdict that stores books author’s names based on their genre:<\/p>\n\n\n\n
from collections import defaultdict\nbooks = defaultdict(str)\nbooks['mystery'] = 'Agatha Christie'\nbooks['science fiction'] = 'Isaac Asimov'\nbooks['horror'] = 'Stephen King'\nprint(books)\n<\/code><\/pre>\n\n\n\nOutput:<\/strong><\/p>\n\n\n\ndefaultdict(<class 'str'=\"\">, {'mystery': 'Agatha Christie', 'science fiction': 'Isaac Asimov', 'horror': 'Stephen King'})<\/code><\/pre>\n\n\n\nIn this example, we specified str as the default_factory parameter, which means any non-existing keys will have an empty string as their value. However, we were able to set up the values for specific keys like ‘mystery,’ ‘science fiction,’ and ‘horror.’<\/p>\n\n\n\n
We can also specify a list as the default_factory to initialize a defaultdict that stores book titles based on their genre.<\/p>\n\n\n\n
books = defaultdict(list)\nbooks['mystery'].append('Murder on the Orient Express')\nbooks['mystery'].append('Death on the Nile')\nbooks['science fiction'].append('Foundation')\nbooks['horror'].append('It')\nprint(books)<\/code><\/pre>\n\n\n\nOutput:<\/strong><\/p>\n\n\n\ndefaultdict(<class 'list'=\"\">, {'mystery': ['Murder on the Orient Express', 'Death on the Nile'], 'science fiction': ['Foundation'], 'horror': ['It']})<\/code><\/pre>\n\n\n\nIn this example, we specified list as the default_factory parameter, which initializes any non-existing keys with an empty list. We then added book titles to specific keys like ‘mystery,’ ‘science fiction,’ and ‘horror.’<\/p>\n\n\n\n
<\/span>Benefits of Defaultdict<\/span><\/h2>\n\n\n\nDefaultdict can be very useful when working with dictionaries because it saves us the pain of checking whether a key exists in a dictionary or not. For instance, suppose we have a program that needs to count the number of times a particular word appears in an input text. In that case, we could use a defaultdict with an int factory to achieve this goal.<\/p>\n\n\n\n
from collections import defaultdict\nwords_count = defaultdict(int)\n\ntext = 'the quick brown fox jumps over the lazy dog'\n\nfor word in text.split():\nwords_count[word] += 1\n\nprint(words_count)<\/code><\/pre>\n\n\n\nOutput:<\/strong><\/p>\n\n\n\ndefaultdict(<class 'int'=\"\">, {'the': 2, 'quick': 1, 'brown': 1, 'fox': 1, 'jumps': 1, 'over': 1, 'lazy': 1, 'dog': 1})<\/code><\/pre>\n\n\n\nIn this example, we initialized words_count as a defaultdict with an int factory. Then, we looped through the input text, splitting it into individual words. For each word, we updated its count in the defaultdict, using the += operator. As a result, we were able to count the number of times each word appeared in the text without having to check whether a key exists or not.<\/p>\n\n\n\n
<\/span>FAQs<\/span><\/h2>\n\n\n\n<\/span>Q: What happens if we try to access a non-existing key in a defaultdict?<\/span><\/h3>\n\n\n\nA: If we try to access a non-existing key in a defaultdict, it returns the default value specified by the default_factory. If the default_factory hasn’t been set, it returns None.<\/p>\n\n\n\n
<\/span>Q: Can we modify the default_factory of a defaultdict after creating it?<\/span><\/h3>\n\n\n\nA: Yes, we can modify the default_factory of a defaultdict by assigning a different callable object to the defaultdict.default_factory attribute.<\/p>\n\n\n\n
<\/span>Q: Is it possible to have a nested defaultdict?<\/span><\/h3>\n\n\n\nA: Yes, we can have a nested defaultdict by setting the default_factory parameter to another defaultdict.<\/p>\n\n\n\n
<\/span>Q: How is a defaultdict different from a regular dictionary?<\/span><\/h3>\n\n\n\nA: A defaultdict is a subclass of the built-in dict class that automatically assigns a default value to any non-existing key. In contrast, a regular dictionary raises a KeyError when trying to access a non-existing key.<\/p>\n\n\n\n
Conclusion<\/strong><\/p>\n\n\n\nThe Python defaultdict is a powerful data structure that can make programming with dictionaries much easier. By providing a default value for any non-existing keys, it allows us to avoid key errors and concentrate on the logic of the program. We can specify any callable object as the default_factory parameter, enabling us to create complex data structures that suit our needs.<\/p>\n","protected":false},"excerpt":{"rendered":"
In Python, we have several built-in data structures like lists, tuples, dictionaries, sets, etc., which make it easy to manipulate […]<\/p>\n","protected":false},"author":3000066,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[3001965,3001966],"tags":[],"class_list":["post-3110","post","type-post","status-publish","format-standard","hentry","category-programming-languages","category-python"],"acf":[],"_links":{"self":[{"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/posts\/3110","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/users\/3000066"}],"replies":[{"embeddable":true,"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/comments?post=3110"}],"version-history":[{"count":0,"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/posts\/3110\/revisions"}],"wp:attachment":[{"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/media?parent=3110"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/categories?post=3110"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thrivemyway.com\/wp-json\/wp\/v2\/tags?post=3110"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}