


Detailed explanation of python using dbm persistent dictionary (python micro database)
In some small Python applications, when a relational database is not required, it is convenient to use a persistent dictionary to store name/value pairs. It is very similar to a Python dictionary. The main difference is that the data is read and written on the disk. . Another difference is that dbm keys and values must be of string type.
1. Choose dbm module
Python supports many dbm modules, unfortunately, the files created by each dbm module are not compatible.
The following table lists these modules:
Module Description
dbm Choose the best dbm module
dbm.dumb uses a simple but portable implementation of the dbm library
dbm.gnu uses the GNU dbm library
Generally, unless a dbm library has special advanced functions, use the dbm module.
2. Create a persistent dictionary
import dbm db = dbm.open('Bookmark', 'c') #添加选项 db['MyBlog'] = 'jonathanlife.sinaapp.com' print(db['MyBlog']) #保存,关闭 db.close()
The open function has three ways to open dbm:
Flag usage
C open the file to read and write it, create the file if necessary
W open the file to read and write it, if File does not exist, it will not be created
N opens the file for reading and writing, but a new blank file is always created
It is also possible to pass another optional argument representing the mode, which holds a set of UNIX file permissions will not be discussed in detail here.
3. Access the persistent dictionary
The object returned from the open function is regarded as a dictionary object. The access method for values is as follows:
db[‘key’] = ‘value’ value = db[‘key’] #删除值: del db[‘key’] #遍历所有key: for key in db.keys(): #your code here
Code example:
import dbm #open existing file db = dbm.open('websites', 'w') #add item db['first_data'] = 'Hello world' #verify the previous item remains if db['first_data'] != None: print('the data exists') else: print('Missing item') #iterate over the keys, may be slow for key in db.keys(): print("Key=",key," value=",db[key]) #delete item del db['first_data'] #close and save to disk db.close()

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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