The basic idea of quick sort:
Split the data to be sorted into two independent parts through one sorting. All the data in one part is smaller than all the data in the other part, and then use this method to sort the two parts of the data. Quick sort is performed separately, and the entire sorting process can be performed recursively, so that the entire data becomes ordered.
Example:
arr = [49,38,04,97,76,13,27,49,55,65], set the first digit 49 as the key value, and find the number smaller than the key value from right to left , assign the found number to the first digit;
arr = [27,38,04,97,76,13,27,49,55,65], and then find the key value from the first digit on the left to the right For large numbers, assign the found number to the last number found from right to left;
arr = [27,38,04,97,76,13,97,49,55,65], and then proceed from right to left Left, from left to right, until left=right, break out of the loop, and assign the key value to some index value. Finally, recurse the groups on both sides.
Code:
def quick_sort(lists, left, right): #快速排序 if left >= right: #当递归调用的分组为1个数时返回列表 return lists key = lists[left] #保存key值,在一轮调用结束时,存到中间值 low = left high = right #供递归调用时使用 while left < right: #通过下面两个循环依次交替赋值并使key值两侧为大小分组 while left < right and lists[right] >= key: right -= 1 lists[left] = lists[right] while left < right and lists[left] <= key: left += 1 lists[right] = lists[left] lists[right] = key quick_sort(lists, low, left-1) #对key值左侧进行排序分组 quick_sort(lists, left+1, high) #对key值右侧进行排序分组 return lists

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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