search
HomeBackend DevelopmentPython TutorialIntroduction to methods of time processing and scheduled tasks in Python3 (with code)

This article brings you an introduction to time processing and timing tasks in Python3 (with code). It has certain reference value. Friends in need can refer to it. I hope it will be helpful to you.

No matter which programming language, time is definitely a very important part. Today we will take a look at how python handles time and python scheduled tasks

Note: This article is about The implementation of the python3 version is slightly different in the python2 version

1. Calculate the dates of tomorrow and yesterday

#! /usr/bin/env python
#coding=utf-8
# 获取今天、昨天和明天的日期
# 引入datetime模块
import datetime 
#计算今天的时间
today = datetime.date.today()
#计算昨天的时间 
yesterday = today - datetime.timedelta(days = 1)
#计算明天的时间
tomorrow = today + datetime.timedelta(days = 1) 
#打印这三个时间
print(yesterday, today, tomorrow)

2. Calculate the previous time

Method 1 :

#! /usr/bin/env python
#coding=utf-8
# 计算上一个的时间
#引入datetime,calendar两个模块
import datetime,calendar
  
last_friday = datetime.date.today() 
oneday = datetime.timedelta(days = 1) 
    
while last_friday.weekday() != calendar.FRIDAY: 
    last_friday -= oneday 
    
print(last_friday.strftime('%A, %d-%b-%Y'))

Method 2: Use modular operation to find the previous Friday

#! /usr/bin/env python
#coding=utf-8
# 借助模运算,可以一次算出需要减去的天数,计算上一个星期五
#同样引入datetime,calendar两个模块
import datetime 
import calendar 
    
today = datetime.date.today() 
target_day = calendar.FRIDAY 
this_day = today.weekday() 
delta_to_target = (this_day - target_day) % 7
last_friday = today - datetime.timedelta(days = delta_to_target) 
    
print(last_friday.strftime("%d-%b-%Y"))

3. Calculate the total playing time of the song

#! /usr/bin/env python
#coding=utf-8
# 获取一个列表中的所有歌曲的播放时间之和 
import datetime 
    
def total_timer(times): 
    td = datetime.timedelta(0) 
    duration = sum([datetime.timedelta(minutes = m, seconds = s) for m, s in times], td) 
    return duration 
    
times1 = [(2, 36), 
          (3, 35), 
          (3, 45), 
          ] 
times2 = [(3, 0), 
          (5, 13), 
          (4, 12), 
          (1, 10), 
          ] 
    
assert total_timer(times1) == datetime.timedelta(0, 596) 
assert total_timer(times2) == datetime.timedelta(0, 815) 
    
print("Tests passed.\n"
      "First test total: %s\n"
      "Second test total: %s" % (total_timer(times1), total_timer(times2)))

4. Repeat a command

#! /usr/bin/env python
#coding=utf-8
# 以需要的时间间隔执行某个命令 
    
import time, os 
    
def re_exe(cmd, inc = 60): 
    while True: 
        os.system(cmd); 
        time.sleep(inc) 
    
re_exe("echo %time%", 5)

5. Scheduled tasks

#! /usr/bin/env python
#coding=utf-8
#这里需要引入三个模块
import time, os, sched 
# 第一个参数确定任务的时间,返回从某个特定的时间到现在经历的秒数 
# 第二个参数以某种人为的方式衡量时间 
schedule = sched.scheduler(time.time, time.sleep) 
def perform_command(cmd, inc): 
    os.system(cmd) 
def timming_exe(cmd, inc = 60): 
    # enter用来安排某事件的发生时间,从现在起第n秒开始启动 
    schedule.enter(inc, 0, perform_command, (cmd, inc)) 
    # 持续运行,直到计划时间队列变成空为止 
    schedule.run()  
print("show time after 10 seconds:") 
timming_exe("echo %time%", 10)

6. Use sched to implement periodic calls

#! /usr/bin/env python
#coding=utf-8
import time, os, sched 
# 第一个参数确定任务的时间,返回从某个特定的时间到现在经历的秒数 
# 第二个参数以某种人为的方式衡量时间 
schedule = sched.scheduler(time.time, time.sleep)   
def perform_command(cmd, inc): 
    # 安排inc秒后再次运行自己,即周期运行 
    schedule.enter(inc, 0, perform_command, (cmd, inc)) 
    os.system(cmd)                         
def timming_exe(cmd, inc = 60): 
    # enter用来安排某事件的发生时间,从现在起第n秒开始启动 
    schedule.enter(inc, 0, perform_command, (cmd, inc)) 
    # 持续运行,直到计划时间队列变成空为止 
    schedule.run() 
print("show time after 10 seconds:") 
timming_exe("echo %time%", 10)

The above is the detailed content of Introduction to methods of time processing and scheduled tasks in Python3 (with code). For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:segmentfault. If there is any infringement, please contact admin@php.cn delete
Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

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 vs. C  : Memory Management and ControlPython vs. C : Memory Management and ControlApr 19, 2025 am 12:17 AM

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 for Scientific Computing: A Detailed LookPython for Scientific Computing: A Detailed LookApr 19, 2025 am 12:15 AM

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.

Python and C  : Finding the Right ToolPython and C : Finding the Right ToolApr 19, 2025 am 12:04 AM

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 for Data Science and Machine LearningPython for Data Science and Machine LearningApr 19, 2025 am 12:02 AM

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.

Learning Python: Is 2 Hours of Daily Study Sufficient?Learning Python: Is 2 Hours of Daily Study Sufficient?Apr 18, 2025 am 12:22 AM

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.

Python for Web Development: Key ApplicationsPython for Web Development: Key ApplicationsApr 18, 2025 am 12:20 AM

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 vs. C  : Exploring Performance and EfficiencyPython vs. C : Exploring Performance and EfficiencyApr 18, 2025 am 12:20 AM

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.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Atom editor mac version download

Atom editor mac version download

The most popular open source editor

SublimeText3 Linux new version

SublimeText3 Linux new version

SublimeText3 Linux latest version

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

SublimeText3 English version

SublimeText3 English version

Recommended: Win version, supports code prompts!

SAP NetWeaver Server Adapter for Eclipse

SAP NetWeaver Server Adapter for Eclipse

Integrate Eclipse with SAP NetWeaver application server.