Python data types: parsing data structures in simple terms

• List (list): A collection that stores ordered elements
• Tuple (tuple): An immutable collection that stores ordered elements
• Set (set): A collection that stores unordered and non-repeating elements
• Dictionary (dict): A collection of key-value pairs

3. Data structure

Data structure is a way of organizing data for efficient storage, retrieval and processing of data. python Provides built-in data structures, including:

• Array (array): A linear data structure that efficiently stores elements of the same type
• Linked list: A linear data structure that uses pointers to link elements
• Stack: A linear data structure that follows the last-in-first-out (FILO) principle
• Queue(queue): A linear data structure that follows the first-in-first-out (FIFO) principle
• Tree (tree): Non-linear data structure with hierarchical structure
• Heap: A tree data structure that satisfies the properties of a heap

4. Data type selection

Choosing the appropriate data type is critical to Python the performance and reliability of your program. The following are the selection criteria:

• The size of the data type: affects memory usage and processing speed
• Collection: used to store collections or single elements
• Variability: Whether to allow dynamic modification of the data structure
• Sequentiality: Whether the elements are arranged in a specific order
• Access mode: Different access methods to the data structure (for example, sequential, random)

5. Common mistakes

Common mistakes when using Python data types include:

• Mixed data types: Store different types of data in one data structure
• Using the wrong data structure: Select a data structure that is not suitable for the required operation
• Data structure limit exceeded: Attempt to store data that exceeds its capacity
• Not properly initialized: The data structure was not initialized to appropriate values ​​before use

6. Best Practices

When using Python data types, follow these best practices:

• Choose the appropriate data type: Carefully select the most appropriate type according to your needs
• Clear data type: Clear data type through type annotation or variable declaration
• Consistency: Maintain consistent data type conventions throughout the program
• Avoid uninitialization: Always initialize data structures before use
• Consider performance: Use efficient data structures on large data sets
• Consider maintainability: Choose data structures that are easy to understand and maintain

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