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Whether you’re a beginner who’s just learning Python data types, or an experienced programmer, this Python data structure cheat sheet will help you understand what data structures are and how to use them effectively.
Python is a versatile and powerful programming language that offers a range of data structures to help manage and manipulate complex data. Understanding this information is crucial for any Python developer, as it allows for more efficient and effective programming.
A Python data structures cheat sheet can be a helpful tool that serves as a quick reference guide in case you get stuck. It’s short and sweet, and it’ll prove to be an invaluable resource in no time.
It covers common operations, advanced methods, tips for choosing the proper data structure for different scenarios, and essential commands you need to know to work with data structures in Python. Keep reading to find out all that this Python data structures cheat sheet has to offer!
What Are the Four Data Structures in Python?
Briefly covering the fundamentals, Python programming language offers four primary data structures, which can be divided into two categories: mutable and immutable.
- Lists (Mutable)
- Dictionaries (Mutable)
- Sets (Mutable)
- Tuples (Immutable)
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Python programming language offers four primary data structures, which can be divided into two categories: mutable and immutable.
What Are Common Operations for Python Data Structures?
Python data structures have common operations and methods that allow you to manipulate and manage elements. Here are some common operations for each data structure:
Accessing Elements
This operation involves accessing individual elements within a data structure. You can access elements in a list or tuple using indexing and in a dictionary or set using keys.
Adding Elements
This operation involves adding new elements to a data structure. You can add elements to a list or set using the “append()” and “add()” methods, respectively. In a dictionary, you can add new key-value pairs using the syntax “dict[key] = value.”
Removing Elements
This operation involves removing elements from a data structure. You can remove elements from a list using the “pop()” or “remove()” method and from a set using the “remove()” method. In a dictionary, you can remove key-value pairs using the “del” keyword.
Sorting Elements
This operation involves arranging elements in ascending or descending order. You can sort a list using the “sort()” method and a set using the “sorted()” function. In a dictionary, you can sort the keys using the sorted() function.
Concatenating Elements
This operation involves combining two or more data structures into one. You can concatenate two lists using the “+” operator and two sets using the “union()” method. In a dictionary, you can use the “update()” method to merge two dictionaries.
Finding Length
This operation involves determining the number of elements in a data structure. You can find the length of a list or tuple or the length of a set or dictionary using the “len()” function.
Here are some examples of how to perform each operation:
Accessing elements:
- my_list[0] – access the first element in a list
- my_tuple[1] – access the second element in a tuple
- my_dict[‘key’] – access the value associated with a key in a dictionary
- my_set.pop() – access and remove an arbitrary element from a set
Adding elements:
- my_list.append(‘new_element’) – add a new element to the end of a list
- my_set.add(‘new_element’) – add a new element to a set
- my_dict[‘new_key’] = ‘new_value’ – add a new key-value pair to a dictionary
Removing elements:
- my_list.pop(2) – remove the element at index 2 from a list
- my_set.remove(‘element’) – remove a specific element from a set
- del my_dict[‘key’] – remove a key-value pair from a dictionary
Sorting elements:
- my_list.sort() – sort a list in ascending order
- sorted(my_set) – sort a set in ascending order
- sorted(my_dict) – sort the keys in a dictionary in ascending order
Concatenating elements:
- my_list3 = my_list1 + my_list2 – concatenate two lists into a new list
- my_set3 = my_set1.union(my_set2) – concatenate two sets into a new set
- my_dict1.update(my_dict2) – concatenate two dictionaries into a new dictionary
Finding length:
- len(my_list) – find the length of a list
- len(my_set) – find the length of a set
- len(my_dict) – find the number of key-value pairs in a dictionary
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What Are Advanced Operations for Python Data Structures?
In addition to the basic operations and methods covered in section III, Python data structures offer more advanced features to manipulate data.
Lists:
- Accessing elements: my_list[0]
- Adding elements: my_list.append(5)
- Removing elements: my_list.remove(5)
- Sorting elements: my_list.sort()
- Concatenating elements: new_list = my_list + other_list
- Finding length: len(my_list)
- Slicing: my_list[1:3]
- Mapping: new_list = list(map(lambda x: x*2, my_list))
- Filtering: new_list = list(filter(lambda x: x > 3, my_list))
- Combining: new_list = my_list.extend(other_list)
- Reversing: my_list.reverse()
- Searching: index = my_list.index(5)
- Traversing: for item in my_list: print(item)
Sets:
- Accessing elements: not applicable
- Adding elements: my_set.add(5)
- Removing elements: my_set.remove(5)
- Sorting elements: not applicable (sets are unordered)
- Concatenating elements: new_set = my_set.union(other_set)
- Finding length: len(my_set)
- Slicing: not applicable
- Mapping: not applicable
- Filtering: new_set = {x for x in my_set if x > 3}
- Combining: my_set.update(other_set)
- Reversing: not applicable (sets are unordered)
- Searching: not applicable
- Traversing: for item in my_set: print(item)
Tuples:
- Accessing elements: my_tuple[0]
- Adding elements: not applicable (tuples are immutable)
- Removing elements: not applicable (tuples are immutable)
- Sorting elements: not applicable (tuples are immutable)
- Concatenating elements: new_tuple = my_tuple + other_tuple
- Finding length: len(my_tuple)
- Slicing: my_tuple[1:3]
- Mapping: new_tuple = tuple(map(lambda x: x*2, my_tuple))
- Filtering: new_tuple = tuple(filter(lambda x: x > 3, my_tuple))
- Combining: not applicable (tuples are immutable)
- Reversing: my_tuple[::-1]
- Searching: index = my_tuple.index(5)
- Traversing: for item in my_tuple: print(item)
Dictionaries:
- Accessing elements: my_dict[‘key’]
- Adding elements: my_dict[‘new_key’] = ‘new_value’
- Removing elements: del my_dict[‘key’]
- Sorting elements: not applicable (dictionaries are unordered)
- Concatenating elements: new_dict = {**my_dict, **other_dict}
- Finding length: len(my_dict)
- Slicing: not applicable
- Mapping: new_dict = {key: value*2 for key, value in my_dict.items()}
- Filtering: new_dict = {key: value for key, value in my_dict.items() if value > 3}
- Combining: not applicable
- Reversing: not applicable (dictionaries are unordered)
- Searching: value = my_dict.get(‘key’, default=None)
- Traversing: for key, value in my_dict.items(): print(key, value)
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Main Takeaways
This Python data structures cheat sheet provides a quick and easy road to understanding the basic concepts and methods of lists, sets, dictionaries, and tuples. With this tool, you can easily manipulate data when handling your programming tasks.
Please check out any of our courses or blogs for more information about Python data structures, as well as other valuable skills and insights.
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- Python Functions With Examples
- Python Libraries for Data Science
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