Learning FastAPI from Scratch: Quickly Understanding the Core Concepts of API Development
API serves as a bridge for communication between different software systems. As a Python web framework, FastAPI has gained popularity due to its advantages such as simplicity, high performance, automatic API documentation generation, type hint support, and async-friendliness. Quick Start: After installing FastAPI and Uvicorn, write a main.py to define routes (e.g., @app.get("/")), then run Uvicorn to access the interface and return JSON data. Core concepts include: routes (URLs mapped to handler functions), HTTP request methods (GET for data retrieval, POST for data submission), three data handling methods (path parameters, query parameters, request bodies), and data validation (auto-validation via Pydantic models). Interactive documentation is automatically generated via Swagger UI (/docs) and ReDoc (/redoc). After mastering the basics, advanced learning can focus on asynchronous development, middleware, and database integration.
Read MoreBinary Search: Applicable Scenarios and Learning Guide for Beginners
This article introduces the binary search algorithm, whose core is to compare the middle element in an ordered array to gradually narrow down the search range and quickly locate the target. It is suitable for scenarios with ordered data, large data volumes, static (rarely modified) content, and the need for rapid search, such as dictionaries or configuration files. The search process uses left and right pointers to determine the middle value mid. Depending on the size of the target relative to the middle value, the pointers are adjusted: if the middle value equals the target, the search is successful; if the target is larger, left is moved right; if smaller, right is moved left, until the target is found or the range is invalid. The core code of the Python iterative implementation uses a loop with left <= right, calculates mid = (left + right) // 2, and handles boundaries to return -1 when the array is empty or the target does not exist. The time complexity is O(log n) (since the range is halved each time), and the space complexity is O(1) (using only constant variables). Key details include expanding the traversal when handling duplicate elements, directly judging single-element arrays, and returning -1 if the target is not found. The "divide and conquer" (reduction and governance) idea of binary search efficiently solves the problem of fast searching in ordered large datasets, making it an important tool in basic algorithms.
Read MoreLearning Bubble Sort from Scratch: Step-by-Step Teaching and Code Implementation
### Summary of Bubble Sort Sorting is the process of rearranging unordered data according to a set of rules. Bubble Sort is a fundamental sorting algorithm whose core principle is to gradually "bubble up" larger elements to the end of the array through adjacent element comparisons and swaps. **Core Idea**: In each iteration, start from the beginning of the array and compare adjacent elements sequentially. If a preceding element is larger than the following one, swap them. After each iteration, the largest element "sinks" to its correct position at the end, reducing the length of the unsorted portion by 1. Repeat until all elements are sorted. **Steps**: The outer loop controls the number of iterations (n-1 times, where n is the array length). The inner loop compares and swaps adjacent elements in each iteration. An optimization is to terminate early if no swaps occur in a round. **Complexity**: Time complexity is O(n²) in the worst case (completely reversed order) and O(n) in the best case (already sorted). Space complexity is O(1) (in-place sorting). **Characteristics and Applications**: It is simple to implement but inefficient (O(n²)). Suitable for small datasets or scenarios with low efficiency requirements (e.g., teaching demonstrations). By embodying the comparison-swap paradigm, Bubble Sort lays the foundation for understanding more complex sorting algorithms.
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