1. File opening
Open mode:
f = open('test.txt','r')
#r, read-only mode, when the file does not exist, an error will be reported
f = open('test. txt','w')
#w, write-only mode, create the file when the file does not exist, clear the original file when the file exists
f = open('test.txt','x')
# x, a new mode in python3. When the file exists, an error will be reported. When the file does not exist, create the file and write
f = open('test.txt','a')
#a, append mode, the file will not When it exists, create the file
Encoding format:
The above opening mode, the default encoding='utf-8', when the file is garbled when opening it, it may be caused by inconsistent encoding formats
At this time, you can adjust the encoding Format to read files
f=open('test.txt','r',encoding='utf-8')
f=open('test.txt','r',encoding='gbk' )
bytes mode:
When the b parameter is used, the file will be opened in byte mode. When the b parameter is not applicable, the file will be opened in character mode
f=open('test.txt','wb' )
f.write(b'xe7xbbxbfxe8x8cxb6')
f.close()
=>Write test.txt in byte mode
n = open('test.txt','r',encoding='utf- 8')
t = n.read()
print (t)
=> There is no b parameter, the file is read in character mode, and it is displayed as green tea
2. File operation
f=open(' test.txt','r',encoding='utf-8')
f.seek()
=>Move the current pointer position to the specified position. When the mode is opened and there is no b parameter, it is as follows Character position movement, when opened with the b parameter, moves the pointer according to the byte position
f.tell()
=> Gets the byte position of the current pointer, regardless of the opening mode
f.flush()
=>Strong brushing. Generally, when writing or modifying a file, it is cached first, and then written to the file when the file is closed. When using this function, the modified content is directly written to the file
f.fileno
=>File descriptor
f.truncate()
=>Truncate all content after the current pointer position
3. File close
Method 1:
f=open('test.txt ','r',encoding='utf-8')
n = f.read()
f.close()
Method 2:
with open('test.txt','r', encoding='utf-8') as f:
n =f.read()
When using with, the close operation of the file will be automatically performed
And, using with, you can open 2 files at the same time:
with open ('test1.txt','r',encoding='utf-8') as f, open('test2.txt','w',encoding='utf-8') as h:
data = f .read()
h.write(data)

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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