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The differences and applications of lists, tuples, sets and dictionaries in Python

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Release： 2023-08-27 14:09:06

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The differences and applications of lists, tuples, sets and dictionaries in Python

The high-level interpreted programming language Python comes with many built-in data structures, including lists, tuples, sets, and dictionaries. These data structures are crucial to the Python programming environment because they provide an efficient way to store and manage data. This article compares and contrasts several data structures, highlighting their advantages, disadvantages, and best usage scenarios to assist developers.

List

A list is an arranged data structure represented by a square part []. Since it is a mutable information structure, you can change any parts as you add them.
You can add, remove or modify entries in the list using built-in methods such as append(), remove() and insert().
Individual content in the list can also be obtained and changed through slicing and sorting strategies, so it is very useful in scenarios where data is constantly changing and heavy functions are running.
Shopping lists are a great way to use lists because you can add, remove, or modify items as needed, and can be used to store lists of values, such as lists of names or numbers.

The Chinese translation of

Example

is:

Example

# Define a list of fruits
fruits = ['apple', 'banana', 'orange']

# Add a new fruit to the end of the list
fruits.append('kiwi')

# Print the contents of the list
print(fruits)  # Output: ['apple', 'banana', 'orange', 'kiwi']

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Tuple

A tuple is an ordered collection of items enclosed in square brackets (). Since it is a permanent information structure, you cannot change any of its parts after they are added.
Once created, the parts of the tuple remain unchanged. However, you can create a new tuple by merging two or more tuples. In Python, it is common to store data in tuples that need to change infrequently.
Tuples can be used, for example, to record the direction of a point on a chart. Tuples are particularly useful for returning some qualities from a function since you might be returning a tuple from a function rather than creating unambiguous factors for everything.

The Chinese translation of

Example

is:

Example

# Define a tuple of names
names = ('Alice', 'Bob', 'Charlie')

# Print the third name in the tuple
print(names[2])  # Output: Charlie

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The Chinese translation of

Set

is:

SET

A collection is an unordered set of distinct components enclosed in curly braces. It is a mutable data structure, so when a collection is created, you can add or remove elements from it. You can also perform set operations such as union, intersection, and difference on sets.
In Python, sets are often used to perform mathematical operations, such as finding the intersection or union of sets and eliminating duplicates.

The Chinese translation of

Example

is:

Example

# Define a set of unique numbers
numbers = {1, 2, 3, 4, 4, 4}

# Print the contents of the set
print(numbers)  # Output: {1, 2, 3, 4}

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dictionary

The curly brace collection of key-value pairs is the basis of the dictionary. It is a mutable information structure, which means you can add, remove, or change components in a word reference after it is created. Index operations can be used to obtain the value of a key.
Dictionaries are commonly used in Python to store data in a structured format. For example, you can use a dictionary to store student details such as name, age, and grade. Dictionaries are also useful for storing configuration settings in programs.

The Chinese translation of

Example

is:

Example

# Define a dictionary of ages
ages = {'Hancock': 25, 'Julie': 30, 'Jamie': 35}

# Print the age of Hancock
print(ages['Hancock'])  # Output: 25

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Comparison Chart

	List	Tuple	set up	dictionary
grammar	[ ]	( )	{ }	{ }
Variable/Immutable	Variable	Immutable	Variable	Variable
Order	Ordered	Ordered	Unordered list	Unordered list
repeat	allow	allow	Not allowed	Not allowed
index	allow	allow	Not allowed	allow
slice	allow	allow	Not allowed	Not allowed
Common operations	Append(), insert(), delete(), pop(), extend()	Concatenation, unpacking, indexing, slicing	add(), remove(), union(), intersection(), difference()	keys(), values(), items(), get()
app	Storing a variable sequence of items	Storing an immutable sequence of items, returning multiple values from a function	Perform a set operation and remove duplicate items from the list	Storage key-value pairs and provide structured access to data
limit	It is slower when processing large lists and takes up more memory than tuples	Elements cannot be added, deleted or modified after creation	Does not preserve order, cannot store duplicates	Keys must be unique and immutable, values can be mutable or immutable

in conclusion

To store and manipulate data efficiently, Python comes with many built-in data structures. The unmistakable qualities of records, tuples, sets, and word references make them suitable for different use cases. By studying the various variations and applications of various data structures, developers can choose the ideal data structure for their specific needs.

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