import module
Import statements are often used in Python to use objects defined in other modules (that is, other .py files).
1) Use __name__
When we write Python library modules, we often run some test statements. When this program is imported as a library, we do not need to run these test statements. One solution is to comment out the test statements in the module before importing. Python has a more elegant solution, which is to use __name__.
The following is a simple library program TestLib.py. When running TestLib.py directly, __name__ is "__main__". If imported, __name__ is "TestLib".
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def lib_func (a):
return a + 10
def lib_func_another(b):
return b + 20
if __name__ == '__main__':
test = 101
PRint(lib_func(test ))
We import the above TestLib in user.py.
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import TestLib
print(TestLib.lib_func(120))
You can try not Use if __name__=='__main__' in TestLib.py and compare the running results.
2) More ways to use import
import TestLib as test # Reference the TestLib module and rename it to t
For example:
For example:
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import TestLib as t
print(t.lib_func(120))
from TestLib import lib_func # Only reference the lib_func object in TestLib and skip TestLib reference field
The advantage of this is to reduce the memory footprint of the referenced module.
For example:
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from Test Lib import lib_func
print(lib_func(120))
from TestLib import * # Reference all objects in TestLib and skip TestLib reference fields
For example:
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from TestLib import *
print(lib_func(120))
Query
1) Query the parameters of the function
When we want to know which parameters a function will receive, we can use the following method Inquire.
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import inspect
print (inspect.getargspec(func))
2) Query the attributes of an object
In addition to using dir() to query the attributes of an object, we can use the following built-in function to confirm whether an object has a certain attribute:
hasattr(obj, attr_name) # attr_name Is a string
For example:
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a = [1,2,3]
print(hasattr( a,'append'))
2) Query the class and class name to which the object belongs
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a = [1, 2, 3]
print a.__class__
print a.__class__.__name__
3) We can use __base __properties To query the parent class of a certain class:
cls.__base__
For example:
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print(list.__base__)
Use Chinese (and other non-ASCII encodings)
Add #coding=utf8 in the first line of the Python program, for example:
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#coding=utf8
print("How are you?")
can also be used in the following ways:
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#-*- coding: UTF-8 -*-
print("How are you?")
represents binary, octal and hexadecimal numbers
In version 2.6 or above, it is represented in the following way
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print(0b1110) # Binary, starting with 0b
print(0o10) # Octal, starting with 0o
print(0x2A) # Hexadecimal, starting with 0x
If yes For earlier versions, you can use the following method:
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print(int("1110", 2))
print(int("10", 8))
print(int("2A", 16))
Comments
Comments in one line can start with #
Multiple lines Comments can start with "' and end with "', such as
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7. print("Hello world!" ) # use print() function
# main
func()
comments should be aligned with the program block where they are located.
Search path
When we import, Python will look for modules in the search path. For example, the above import TestLib requires TestLib.py to be in the search path.
We can view the search path through the following methods:
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import sys
print(sys.path)
We can add or delete elements in sys.path while Python is running. On the other hand, we can increase the search path for Python by adding the PYTHONPATH environment variable in the shell.
Now we add /home/vamei/mylib to the search path:
$export PYTHONPATH=$PYTHONPATH:/home/vamei/mylib
You can add the front line of command to ~/.bashrc. In this way, we change the search path in the long term.
Combining script and command line
You can use the following method to run a Python script. After the script is finished running, directly enter the Python command line. The advantage of this is that the script object will not be cleared and can be called directly through the command line.
$python -i script.py
Install non-standard packages
Python’s standard library is installed along with Python. When we need non-standard packages, we must install them first.
1) Use linux repository (Linux environment)
This is a good starting point for installing Python add-on packages. You can search for possible Python packages in the Linux repository (for example, search for matplot in the Ubuntu Software Center).
2) Use pip. pip is Python's own package management program. It connects to the Python repository and searches for packages that may exist in it.
For example, use the following method to install, uninstall or upgrade web.py:
$pip install web.py
$pip uninstall web.py
$pip install –upgrade web.py
If your Python is installed in In a non-standard path (use $which python to confirm the path of the python executable file), such as /home/vamei/util/python/bin, you can use the following method to set the path of the pip installation package:
$ pip install –install-option=”–prefix=/home/vamei/util/” web.py
3) Compile from source code
If none of the above methods can find the library you want, you may need to start from the source code Compile. Google is often the best place to start.
The above is the content of Python Quick Tutorial (Supplement 02): Python Tips. For more related content, please pay attention to the PHP Chinese website (www.php.cn)!
Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

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.


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