Emulators as an Emerging Best Practice for API ProvidersCisco DevNet
The document discusses API emulators as an emerging best practice for API providers. It describes Stève Sfartz's background and role at Cisco developing tools for developers. It then discusses how emulators can enhance the developer experience by allowing local testing and debugging of APIs without access to production services. Specific examples of emulators created for Cisco's Tropo and Webex APIs are presented, along with lessons learned around their development and use cases. The presentation concludes by advocating for API providers to consider adding emulators to better support their developer communities.
This document provides an introduction to Python programming. It discusses that Python is an interpreted, object-oriented, high-level programming language with simple syntax. It then covers the need for programming languages, different types of languages, and translators like compilers, interpreters, assemblers, linkers, and loaders. The document concludes by discussing why Python is popular for web development, software development, data science, and more.
The document discusses serverless computing and Kotlin functions. It introduces Fn Project, an open source container-native platform for serverless/function-as-a-service (FaaS) computing. Fn Project supports multiple languages like Go, Java, Python, and Kotlin and can run on Kubernetes as well as cloud providers and on-premises. The document demonstrates how to write Kotlin functions that can be deployed to Fn Project and Exoscale, a European cloud provider that supports Fn Project.
Возможности интерпретатора Python в NX-OSCisco Russia
The document discusses a webinar presented by Cisco TAC Engineer Anton Tugai about the capabilities of the Python interpreter in NX-OS. Some key points:
- Tugai gave a presentation on trends in Cisco SDN and current solutions.
- The webinar covered an introduction to Python, how Python is integrated into NX-OS, examples, and a demonstration.
- Native Python interpreter is available on Nexus switches starting from certain software versions, allowing Python scripts to run directly on the switch and execute CLI commands.
Introduction to Python Programming BasicsDhana malar
Python is a popular high-level programming language that can be used for a wide range of applications from simple scripts to complex machine learning programs. It has a simple syntax, extensive standard library, and support for code reuse through modules and packages. Some key strengths of Python include its huge collection of standard libraries for tasks like machine learning, web development, scientific computing, and more. It is also an interpreted language, making it easy to learn and use for both simple and complex programming tasks.
This document provides an introduction to Jupyter Notebook and Azure Machine Learning Studio. It discusses popular programming languages like Python, R, and Julia that can be used with these tools. It also summarizes key features of Jupyter Notebook like code cells, kernels, and cloud deployment. Demo code examples are shown for integrating Python and R with Azure ML to fetch and transform data.
This document provides an introduction to Jupyter Notebook and Azure Machine Learning Studio. It discusses popular programming languages like Python, R, and Julia that can be used with these tools. It also summarizes key features of Jupyter Notebook like code cells, kernels, and cloud deployment. Examples are given of using Python and R with Azure ML to fetch and transform data in Jupyter notebooks.
This document provides an overview of the Python programming language. It discusses Python's history, key features such as being easy to use, scalable, high-level, object-oriented, interpreted, and having a rich core library. It also covers Python's uses in areas like web development, databases, GUI programming, and more. The document is intended to introduce readers to Python and provide context for a book on making use of the language.
Emulators as an Emerging Best Practice for API ProvidersCisco DevNet
The document discusses API emulators as an emerging best practice for API providers. It describes Stève Sfartz's background and role at Cisco developing tools for developers. It then discusses how emulators can enhance the developer experience by allowing local testing and debugging of APIs without access to production services. Specific examples of emulators created for Cisco's Tropo and Webex APIs are presented, along with lessons learned around their development and use cases. The presentation concludes by advocating for API providers to consider adding emulators to better support their developer communities.
This document provides an introduction to Python programming. It discusses that Python is an interpreted, object-oriented, high-level programming language with simple syntax. It then covers the need for programming languages, different types of languages, and translators like compilers, interpreters, assemblers, linkers, and loaders. The document concludes by discussing why Python is popular for web development, software development, data science, and more.
The document discusses serverless computing and Kotlin functions. It introduces Fn Project, an open source container-native platform for serverless/function-as-a-service (FaaS) computing. Fn Project supports multiple languages like Go, Java, Python, and Kotlin and can run on Kubernetes as well as cloud providers and on-premises. The document demonstrates how to write Kotlin functions that can be deployed to Fn Project and Exoscale, a European cloud provider that supports Fn Project.
Возможности интерпретатора Python в NX-OSCisco Russia
The document discusses a webinar presented by Cisco TAC Engineer Anton Tugai about the capabilities of the Python interpreter in NX-OS. Some key points:
- Tugai gave a presentation on trends in Cisco SDN and current solutions.
- The webinar covered an introduction to Python, how Python is integrated into NX-OS, examples, and a demonstration.
- Native Python interpreter is available on Nexus switches starting from certain software versions, allowing Python scripts to run directly on the switch and execute CLI commands.
Introduction to Python Programming BasicsDhana malar
Python is a popular high-level programming language that can be used for a wide range of applications from simple scripts to complex machine learning programs. It has a simple syntax, extensive standard library, and support for code reuse through modules and packages. Some key strengths of Python include its huge collection of standard libraries for tasks like machine learning, web development, scientific computing, and more. It is also an interpreted language, making it easy to learn and use for both simple and complex programming tasks.
This document provides an introduction to Jupyter Notebook and Azure Machine Learning Studio. It discusses popular programming languages like Python, R, and Julia that can be used with these tools. It also summarizes key features of Jupyter Notebook like code cells, kernels, and cloud deployment. Demo code examples are shown for integrating Python and R with Azure ML to fetch and transform data.
This document provides an introduction to Jupyter Notebook and Azure Machine Learning Studio. It discusses popular programming languages like Python, R, and Julia that can be used with these tools. It also summarizes key features of Jupyter Notebook like code cells, kernels, and cloud deployment. Examples are given of using Python and R with Azure ML to fetch and transform data in Jupyter notebooks.
This document provides an overview of the Python programming language. It discusses Python's history, key features such as being easy to use, scalable, high-level, object-oriented, interpreted, and having a rich core library. It also covers Python's uses in areas like web development, databases, GUI programming, and more. The document is intended to introduce readers to Python and provide context for a book on making use of the language.
LAS16-108: JerryScript and other scripting languages for IoTLinaro
LAS16-108: JerryScript and other scripting languages for IoT
Speakers: Paul Sokolovsky
Date: September 26, 2016
★ Session Description ★
Overview of small-size/low-resource VHLL (very high-level languages)/scripting languages available for embedded/IoT usage (JavaScript, Python, Lua, etc.). Typical/possible usage scenarios and benefits. Challenges of running VHLLs in deeply embedded/very resource-constrained environments. Progress reports on porting JerryScript to Zephyr. (Possibly, architecture comparison of JerryScript and MicroPython).
★ Resources ★
Etherpad: pad.linaro.org/p/las16-108
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-108/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
Python is an increasingly popular programming language due to its emphasis on code readability and ease of writing programs with fewer lines of code compared to languages like C++ and Java. It supports object-oriented, imperative, and functional programming. Python is interpreted, platform independent, has a simple syntax closer to English, and includes a vast standard library. While Python programs are generally more concise than languages like Java, it is slower than C/C++. Python is commonly used for scripting, artificial intelligence, and natural language processing.
Top programming Languages in software Industry companiesKiran Patil
top Programming Languages used in software companies,features of all programming languages,java,
JavaScript,PHP, C# ,Typescript ,Best Programming Language
Top Salary based on Programming skill ,
current Running Tools and Technology in Market
Android use for Mobile Application
AngularJS is used for Many web/mobile Application
PHP and Python are most trending languages
The document provides an overview of using Python for bioinformatics, discussing what Python is, why it is useful for bioinformatics, how to set up Python in integrated development environments like Eclipse with PyDev, how to share code using Git and GitHub, and includes examples of Hello World and bioinformatics programs in Python. It introduces Python and argues it is well-suited for bioinformatics due to its extensive standard libraries, ease of use, and wide adoption in science. The document demonstrates how to install Python, set up an IDE, create and run simple Python programs, and use version control with Git and GitHub to collaborate on projects.
This document provides information about Jupyter Notebook, including:
- Jupyter Notebook is an open-source web application for creating and sharing documents containing live code, equations, visualizations, and narrative text.
- It works locally on localhost port 8888 and the easiest way to install it is through Anaconda which includes Jupyter Notebook and popular Python libraries.
- Notebooks use kernels to run code in different programming languages, with IPython being the default Python kernel.
Python is a versatile and widely-used high-level programming language known for its simplicity, readability, and extensive library support. Created by Guido van Rossum and first released in 1991, Python has since gained immense popularity across various domains, including web development, data science, scientific computing, artificial intelligence, and more. In this comprehensive description, we'll delve into Python's history, features, applications, and its vibrant community, highlighting why it continues to be a preferred choice for developers worldwide.
Table of Contents
Introduction to Python
Python's History and Evolution
Python's Key Features
3.1. Readability and Simplicity
3.2. High-level Language
3.3. Interpreted and Dynamic
3.4. Cross-platform Compatibility
3.5. Rich Standard Library
3.6. Community Support
Python's Application Domains
4.1. Web Development
4.2. Data Science and Machine Learning
4.3. Scientific Computing
4.4. Automation and Scripting
4.5. Game Development
4.6. Desktop Applications
Python Development Environments
5.1. IDLE
5.2. PyCharm
5.3. Jupyter Notebook
5.4. Visual Studio Code
Getting Started with Python
6.1. Installing Python
6.2. Your First Python Program
Python Syntax and Basic Concepts
7.1. Variables and Data Types
7.2. Conditional Statements
7.3. Loops
7.4. Functions
7.5. Exception Handling
Working with Python Libraries
8.1. NumPy
8.2. Pandas
8.3. Matplotlib
8.4. Scikit-Learn
Python and Web Development
9.1. Frameworks (Django, Flask)
9.2. Front-end Integration (HTML/CSS)
9.3. Database Interaction (SQL, NoSQL)
Python in Data Science
10.1. Data Analysis with Pandas
10.2. Data Visualization with Matplotlib and Seaborn
10.3. Machine Learning with Scikit-Learn
10.4. Deep Learning with TensorFlow and PyTorch
Scientific Computing with Python
11.1. Scientific Libraries (SciPy, SymPy)
11.2. Plotting and Visualization (Matplotlib)
Automation and Scripting
12.1. Automating Tasks
12.2. Scripting for System Administration
Game Development with Python
13.1. Pygame
13.2. Unity and Unreal Engine Integration
Desktop Applications with Python
14.1. Tkinter
14.2. PyQt
Python's Ecosystem and Package Management
Python Best Practices
16.1. Code Readability (PEP 8)
16.2. Documentation and Comments
16.3. Testing (Unit Testing, pytest)
16.4. Version Control (Git)
Python's Future and Trends
Conclusion
1. Introduction to Python
Python is a general-purpose, high-level programming language that was designed with a focus on code readability and simplicity. It uses an elegant and straightforward syntax that makes it easy for developers to express their ideas effectively, reducing the cost of program maintenance. Python's philosophy emphasizes the importance of code clarity and readability, which is encapsulated in the Zen of Python (PEP 20).
The language has gained immense popularity due to its versatility and a rich ecosystem of libraries and frameworks. Python is renowned for its vibrant community and extensive documentation, making it in p
The document discusses Function as a Service (FaaS) and the Fn Project open source container-native serverless platform. It describes how FaaS allows developers to write small, independent pieces of code (functions) that can be easily deployed, invoked and scaled independently. The Fn Project provides a way for developers to build and run FaaS workloads across any infrastructure using open standards like Docker and Kubernetes. Upcoming Oracle Functions will provide a serverless platform based on Fn Project that integrates with Oracle Cloud and offers features like auto-scaling, pay-per-use billing and container-native deployment.
Certainly! Here's a detailed 3000-word description of Python:
# Python: A Comprehensive Overview
Python is a high-level, versatile, and dynamically-typed programming language known for its simplicity and readability. Created by Guido van Rossum in the late 1980s, Python has since become one of the most popular programming languages worldwide. In this comprehensive overview, we will delve into the key aspects of Python, from its history and design philosophy to its syntax, libraries, and real-world applications.
## **History and Evolution of Python**
Python's history dates back to December 1989 when Guido van Rossum, a Dutch programmer, began working on it as a side project during his Christmas holidays. His aim was to create a language that emphasized code readability and allowed developers to express their ideas in fewer lines of code compared to other languages like C++ or Perl.
The first official Python release, Python 0.9.0, was released in February 1991. Python's name was inspired by Guido's love for the British comedy group Monty Python. Despite its humorous origins, Python quickly gained popularity in the software development community.
Python's major versions include Python 1.0 (1994), Python 2.0 (2000), Python 3.0 (2008), and the subsequent 3.x releases. The transition from Python 2 to Python 3 was a significant milestone in Python's history, as it involved breaking compatibility with Python 2 to introduce improvements and address some language inconsistencies. Python 2 reached its end of life on January 1, 2020, and Python 3 is now the standard and recommended version for new projects.
## **Design Philosophy: The Zen of Python**
Python's success can be attributed, in part, to its clear and guiding design principles, often referred to as "The Zen of Python" or "PEP 20" (Python Enhancement Proposal 20). These principles encapsulate the language's philosophy and provide a framework for writing clean, readable, and maintainable code. Some notable principles from "The Zen of Python" include:
- **Readability Counts:** Code should be easy to read and understand. Python's syntax enforces this with its use of indentation for block structure.
- **Simple is Better Than Complex:** Python encourages simplicity in both code design and implementation. It favors straightforward solutions over convoluted ones.
- **Explicit is Better Than Implicit:** Code should be explicit and not rely on hidden or magical behavior. This principle promotes code clarity and predictability.
- **There Should Be One-- and Preferably Only One --Obvious Way to Do It:** Python aims to provide a single, clear way to perform a specific task to reduce confusion and make code more consistent.
- **Errors Should Never Pass Silently:** Python encourages robust error handling and reporting to help developers identify and fix issues promptly.
## **Python Syntax and Language Features**
Python's syntax is known for its simplicity and readability. Here are some key languag
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at: https://www.youtube.com/watch?feature=player_embedded&v=_LxfIQuFALY
Introduction to python history and platformsKirti Verma
This document provides an introduction to Python and discusses popular tools used in data science, the evolution of Python, advantages of using Python, coding environments including Integrated Development Environments (IDEs) like PyCharm, Jupyter Notebook, and Spyder. It describes features of these IDEs and how they can be used for coding, debugging, and data analysis in Python.
This document provides an introduction to Python programming. It discusses the history and origins of Python, its key features and applications. Some of the main points covered include:
- Python was created in the late 1980s by Guido van Rossum and takes influence from other languages like ABC, Modula-3, C, C++ and Unix shell scripts.
- Python is an interpreted, object-oriented scripting language that is designed to be highly readable. It has applications in systems programming, GUIs, web development, data analysis, scientific computing and more.
- The document outlines Python's technical strengths like being free, portable, powerful, easy to use and learn. It also covers basics like variables,
This document discusses code generation in .NET. It begins by outlining some common problems developers face when dealing with large amounts of repetitive code. It then discusses various approaches to solving this problem, including hand coding everything, fully generic design, and using a combination of tools including code generation. The rest of the document discusses specific code generation tools for .NET like StringBuilder, CodeSnippets, XSLT, Reflection.Emit, EnvDTE, CodeDom, and T4. It also discusses pros and cons of each approach and provides examples of using code generation in different real world scenarios.
Srikanth Pilli has over 6 years of experience in embedded software development. He has expertise in C/C++, Python, Linux kernel driver development, video streaming, and networking. He has worked on projects involving home automation, surveillance systems, and embedded device development. His skills include embedded Linux systems, microcontroller programming, real-time protocols, and tools like Git. He holds an M.Tech in embedded systems and postgraduate diplomas in embedded systems and electronics.
This document discusses compiler design and how compilers work. It begins with prerequisites and definitions of compilers and their origins. It then describes the architecture of compilers, including lexical analysis, parsing, semantic analysis, code optimization, and code generation. It explains how compilers translate high-level code into machine-executable code. In conclusions, it summarizes that compilers translate code without changing meaning and aim to make code efficient. References for further reading on compiler design principles are also provided.
The document provides information about Mohan Arumugam's profile and background as a technologies specialist and consultant. It then provides details about the history and development of the Python programming language, including:
- Python is named after the Monty Python comedy group, not the snake.
- Key releases and features added in each major Python version from 0.9.0 to 3.12.
- Python's growing popularity and widespread adoption in various domains like web development, data science, AI, and automation.
- Details about Python 3.12, including new features like generic classes/functions, f-string grammar updates, and security improvements.
The document discusses software and programming concepts for IoT systems. It introduces the Raspberry Pi single board computer and how it can be used for IoT applications. Blockly and Python are presented as programming tools for IoT. Finally, a model IoT home automation system is demonstrated using sensors, actuators and single board computers connected through a home gateway.
Compilers can have a huge effect on software efficiency and performance by changing what user experiences are possible and reducing CPU and resource usage. They work by parsing code, generating machine-friendly representations, and emitting optimized machine code. As web programming grew in complexity, developers started building more efficient compilers for dynamic languages to preserve rapid development workflows while improving performance. There are various approaches to building compilers like interpreters, transpilers, using backends like LLVM, and fully custom solutions. The best approach depends on goals, constraints, and tradeoffs around control, performance, and development effort. Optimization focuses should include memory usage, caching, and runtime layout. Future areas may include database query compilation for real-time analytics on large datasets.
Understand the Trade-offs Using Compilers for Java ApplicationsC4Media
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2QCmmJ0.
Mark Stoodley examines some of the strengths and weaknesses of the different Java compilation technologies, if one was to apply them in isolation. Stoodley discusses how production JVMs are assembling a combination of these tools that work together to provide excellent performance across the large spectrum of applications written in Java and JVM based languages. Filmed at qconsf.com.
Mark Stoodley joined IBM Canada to build Java JIT compilers for production use and led the team that delivered AOT compilation in the IBM SDK for Java 6. He spent the last five years leading the effort to open source nearly 4.3 million lines of source code from the IBM J9 Java Virtual Machine to create the two open source projects Eclipse OMR and Eclipse OpenJ9, and now co-leads both projects.
LAS16-108: JerryScript and other scripting languages for IoTLinaro
LAS16-108: JerryScript and other scripting languages for IoT
Speakers: Paul Sokolovsky
Date: September 26, 2016
★ Session Description ★
Overview of small-size/low-resource VHLL (very high-level languages)/scripting languages available for embedded/IoT usage (JavaScript, Python, Lua, etc.). Typical/possible usage scenarios and benefits. Challenges of running VHLLs in deeply embedded/very resource-constrained environments. Progress reports on porting JerryScript to Zephyr. (Possibly, architecture comparison of JerryScript and MicroPython).
★ Resources ★
Etherpad: pad.linaro.org/p/las16-108
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-108/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
Python is an increasingly popular programming language due to its emphasis on code readability and ease of writing programs with fewer lines of code compared to languages like C++ and Java. It supports object-oriented, imperative, and functional programming. Python is interpreted, platform independent, has a simple syntax closer to English, and includes a vast standard library. While Python programs are generally more concise than languages like Java, it is slower than C/C++. Python is commonly used for scripting, artificial intelligence, and natural language processing.
Top programming Languages in software Industry companiesKiran Patil
top Programming Languages used in software companies,features of all programming languages,java,
JavaScript,PHP, C# ,Typescript ,Best Programming Language
Top Salary based on Programming skill ,
current Running Tools and Technology in Market
Android use for Mobile Application
AngularJS is used for Many web/mobile Application
PHP and Python are most trending languages
The document provides an overview of using Python for bioinformatics, discussing what Python is, why it is useful for bioinformatics, how to set up Python in integrated development environments like Eclipse with PyDev, how to share code using Git and GitHub, and includes examples of Hello World and bioinformatics programs in Python. It introduces Python and argues it is well-suited for bioinformatics due to its extensive standard libraries, ease of use, and wide adoption in science. The document demonstrates how to install Python, set up an IDE, create and run simple Python programs, and use version control with Git and GitHub to collaborate on projects.
This document provides information about Jupyter Notebook, including:
- Jupyter Notebook is an open-source web application for creating and sharing documents containing live code, equations, visualizations, and narrative text.
- It works locally on localhost port 8888 and the easiest way to install it is through Anaconda which includes Jupyter Notebook and popular Python libraries.
- Notebooks use kernels to run code in different programming languages, with IPython being the default Python kernel.
Python is a versatile and widely-used high-level programming language known for its simplicity, readability, and extensive library support. Created by Guido van Rossum and first released in 1991, Python has since gained immense popularity across various domains, including web development, data science, scientific computing, artificial intelligence, and more. In this comprehensive description, we'll delve into Python's history, features, applications, and its vibrant community, highlighting why it continues to be a preferred choice for developers worldwide.
Table of Contents
Introduction to Python
Python's History and Evolution
Python's Key Features
3.1. Readability and Simplicity
3.2. High-level Language
3.3. Interpreted and Dynamic
3.4. Cross-platform Compatibility
3.5. Rich Standard Library
3.6. Community Support
Python's Application Domains
4.1. Web Development
4.2. Data Science and Machine Learning
4.3. Scientific Computing
4.4. Automation and Scripting
4.5. Game Development
4.6. Desktop Applications
Python Development Environments
5.1. IDLE
5.2. PyCharm
5.3. Jupyter Notebook
5.4. Visual Studio Code
Getting Started with Python
6.1. Installing Python
6.2. Your First Python Program
Python Syntax and Basic Concepts
7.1. Variables and Data Types
7.2. Conditional Statements
7.3. Loops
7.4. Functions
7.5. Exception Handling
Working with Python Libraries
8.1. NumPy
8.2. Pandas
8.3. Matplotlib
8.4. Scikit-Learn
Python and Web Development
9.1. Frameworks (Django, Flask)
9.2. Front-end Integration (HTML/CSS)
9.3. Database Interaction (SQL, NoSQL)
Python in Data Science
10.1. Data Analysis with Pandas
10.2. Data Visualization with Matplotlib and Seaborn
10.3. Machine Learning with Scikit-Learn
10.4. Deep Learning with TensorFlow and PyTorch
Scientific Computing with Python
11.1. Scientific Libraries (SciPy, SymPy)
11.2. Plotting and Visualization (Matplotlib)
Automation and Scripting
12.1. Automating Tasks
12.2. Scripting for System Administration
Game Development with Python
13.1. Pygame
13.2. Unity and Unreal Engine Integration
Desktop Applications with Python
14.1. Tkinter
14.2. PyQt
Python's Ecosystem and Package Management
Python Best Practices
16.1. Code Readability (PEP 8)
16.2. Documentation and Comments
16.3. Testing (Unit Testing, pytest)
16.4. Version Control (Git)
Python's Future and Trends
Conclusion
1. Introduction to Python
Python is a general-purpose, high-level programming language that was designed with a focus on code readability and simplicity. It uses an elegant and straightforward syntax that makes it easy for developers to express their ideas effectively, reducing the cost of program maintenance. Python's philosophy emphasizes the importance of code clarity and readability, which is encapsulated in the Zen of Python (PEP 20).
The language has gained immense popularity due to its versatility and a rich ecosystem of libraries and frameworks. Python is renowned for its vibrant community and extensive documentation, making it in p
The document discusses Function as a Service (FaaS) and the Fn Project open source container-native serverless platform. It describes how FaaS allows developers to write small, independent pieces of code (functions) that can be easily deployed, invoked and scaled independently. The Fn Project provides a way for developers to build and run FaaS workloads across any infrastructure using open standards like Docker and Kubernetes. Upcoming Oracle Functions will provide a serverless platform based on Fn Project that integrates with Oracle Cloud and offers features like auto-scaling, pay-per-use billing and container-native deployment.
Certainly! Here's a detailed 3000-word description of Python:
# Python: A Comprehensive Overview
Python is a high-level, versatile, and dynamically-typed programming language known for its simplicity and readability. Created by Guido van Rossum in the late 1980s, Python has since become one of the most popular programming languages worldwide. In this comprehensive overview, we will delve into the key aspects of Python, from its history and design philosophy to its syntax, libraries, and real-world applications.
## **History and Evolution of Python**
Python's history dates back to December 1989 when Guido van Rossum, a Dutch programmer, began working on it as a side project during his Christmas holidays. His aim was to create a language that emphasized code readability and allowed developers to express their ideas in fewer lines of code compared to other languages like C++ or Perl.
The first official Python release, Python 0.9.0, was released in February 1991. Python's name was inspired by Guido's love for the British comedy group Monty Python. Despite its humorous origins, Python quickly gained popularity in the software development community.
Python's major versions include Python 1.0 (1994), Python 2.0 (2000), Python 3.0 (2008), and the subsequent 3.x releases. The transition from Python 2 to Python 3 was a significant milestone in Python's history, as it involved breaking compatibility with Python 2 to introduce improvements and address some language inconsistencies. Python 2 reached its end of life on January 1, 2020, and Python 3 is now the standard and recommended version for new projects.
## **Design Philosophy: The Zen of Python**
Python's success can be attributed, in part, to its clear and guiding design principles, often referred to as "The Zen of Python" or "PEP 20" (Python Enhancement Proposal 20). These principles encapsulate the language's philosophy and provide a framework for writing clean, readable, and maintainable code. Some notable principles from "The Zen of Python" include:
- **Readability Counts:** Code should be easy to read and understand. Python's syntax enforces this with its use of indentation for block structure.
- **Simple is Better Than Complex:** Python encourages simplicity in both code design and implementation. It favors straightforward solutions over convoluted ones.
- **Explicit is Better Than Implicit:** Code should be explicit and not rely on hidden or magical behavior. This principle promotes code clarity and predictability.
- **There Should Be One-- and Preferably Only One --Obvious Way to Do It:** Python aims to provide a single, clear way to perform a specific task to reduce confusion and make code more consistent.
- **Errors Should Never Pass Silently:** Python encourages robust error handling and reporting to help developers identify and fix issues promptly.
## **Python Syntax and Language Features**
Python's syntax is known for its simplicity and readability. Here are some key languag
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at: https://www.youtube.com/watch?feature=player_embedded&v=_LxfIQuFALY
Introduction to python history and platformsKirti Verma
This document provides an introduction to Python and discusses popular tools used in data science, the evolution of Python, advantages of using Python, coding environments including Integrated Development Environments (IDEs) like PyCharm, Jupyter Notebook, and Spyder. It describes features of these IDEs and how they can be used for coding, debugging, and data analysis in Python.
This document provides an introduction to Python programming. It discusses the history and origins of Python, its key features and applications. Some of the main points covered include:
- Python was created in the late 1980s by Guido van Rossum and takes influence from other languages like ABC, Modula-3, C, C++ and Unix shell scripts.
- Python is an interpreted, object-oriented scripting language that is designed to be highly readable. It has applications in systems programming, GUIs, web development, data analysis, scientific computing and more.
- The document outlines Python's technical strengths like being free, portable, powerful, easy to use and learn. It also covers basics like variables,
This document discusses code generation in .NET. It begins by outlining some common problems developers face when dealing with large amounts of repetitive code. It then discusses various approaches to solving this problem, including hand coding everything, fully generic design, and using a combination of tools including code generation. The rest of the document discusses specific code generation tools for .NET like StringBuilder, CodeSnippets, XSLT, Reflection.Emit, EnvDTE, CodeDom, and T4. It also discusses pros and cons of each approach and provides examples of using code generation in different real world scenarios.
Srikanth Pilli has over 6 years of experience in embedded software development. He has expertise in C/C++, Python, Linux kernel driver development, video streaming, and networking. He has worked on projects involving home automation, surveillance systems, and embedded device development. His skills include embedded Linux systems, microcontroller programming, real-time protocols, and tools like Git. He holds an M.Tech in embedded systems and postgraduate diplomas in embedded systems and electronics.
This document discusses compiler design and how compilers work. It begins with prerequisites and definitions of compilers and their origins. It then describes the architecture of compilers, including lexical analysis, parsing, semantic analysis, code optimization, and code generation. It explains how compilers translate high-level code into machine-executable code. In conclusions, it summarizes that compilers translate code without changing meaning and aim to make code efficient. References for further reading on compiler design principles are also provided.
The document provides information about Mohan Arumugam's profile and background as a technologies specialist and consultant. It then provides details about the history and development of the Python programming language, including:
- Python is named after the Monty Python comedy group, not the snake.
- Key releases and features added in each major Python version from 0.9.0 to 3.12.
- Python's growing popularity and widespread adoption in various domains like web development, data science, AI, and automation.
- Details about Python 3.12, including new features like generic classes/functions, f-string grammar updates, and security improvements.
The document discusses software and programming concepts for IoT systems. It introduces the Raspberry Pi single board computer and how it can be used for IoT applications. Blockly and Python are presented as programming tools for IoT. Finally, a model IoT home automation system is demonstrated using sensors, actuators and single board computers connected through a home gateway.
Compilers can have a huge effect on software efficiency and performance by changing what user experiences are possible and reducing CPU and resource usage. They work by parsing code, generating machine-friendly representations, and emitting optimized machine code. As web programming grew in complexity, developers started building more efficient compilers for dynamic languages to preserve rapid development workflows while improving performance. There are various approaches to building compilers like interpreters, transpilers, using backends like LLVM, and fully custom solutions. The best approach depends on goals, constraints, and tradeoffs around control, performance, and development effort. Optimization focuses should include memory usage, caching, and runtime layout. Future areas may include database query compilation for real-time analytics on large datasets.
Understand the Trade-offs Using Compilers for Java ApplicationsC4Media
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2QCmmJ0.
Mark Stoodley examines some of the strengths and weaknesses of the different Java compilation technologies, if one was to apply them in isolation. Stoodley discusses how production JVMs are assembling a combination of these tools that work together to provide excellent performance across the large spectrum of applications written in Java and JVM based languages. Filmed at qconsf.com.
Mark Stoodley joined IBM Canada to build Java JIT compilers for production use and led the team that delivered AOT compilation in the IBM SDK for Java 6. He spent the last five years leading the effort to open source nearly 4.3 million lines of source code from the IBM J9 Java Virtual Machine to create the two open source projects Eclipse OMR and Eclipse OpenJ9, and now co-leads both projects.
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
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6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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6. In Python strings the backslash (\) is a special character which announces that the next character has a different meaning, e.g., \n (the newline character) starts a new output line.
7. Positional arguments are the ones whose meaning is dictated by their position, e.g., the second argument is outputted after the first, the third is outputted after the second, etc.
8. Keyword arguments are the ones whose meaning is not dictated by their location, but by a special word (keyword) used to identify them.
9. The end and sep parameters can be used for formatting the output of the print() function. The sep parameter specifies the separator between the outputted arguments (e.g., print("H", "E", "L", "L", "O", sep="-"), whereas the end parameter specifies what to print at the end of the print statement.
Computer programming is the act of composing the selected programming language's elements in the order that will cause the desired effect. The effect could be different in every specific case - it's up to the programmer's imagination, knowledge and experience.
Of course, such a composition has to be correct in many senses:
alphabetically - a program needs to be written in a recognizable script, such as Roman, Cyrillic, etc.
lexically - each programming language has its dictionary and you need to master it; thankfully, it's much simpler and smaller than the dictionary of any natural language;
syntactically - each language has its rules and they must be obeyed;
semantically - the program has to make sense.
Unfortunately, a programmer can also make mistakes with each of the above four senses. Each of them can cause the program to become completely useless.
Let's assume that you've successfully written a program. How do we persuade the computer to execute it? You have to render your program into machine language. Luckily, the translation can be done by a computer itself, making the whole process fast and efficient.
What does this all mean for you?
Python is an interpreted language. This means that it inherits all the described advantages and disadvantages. Of course, it adds some of its unique features to both sets.
If you want to program in Python, you'll need the Python interpreter. You won't be able to run your code without it. Fortunately, Python is free. This is one of its most important advantages.
Due to historical reasons, languages designed to be utilized in the interpretation manner are often called scripting languages, while the source programs encoded using them are called scripts.
DRAWBACKS
- it's not a speed demon - Python does not deliver exceptional performance;
- in some cases it may be resistant to some simpler testing techniques - this may mean that debugging Python's code can be more difficult than with other languages; fortunately, making mistakes is always harder in Python.
it's easy to learn - the time needed to learn Python is shorter than for many other languages; this means that it's possible to start the actual programming faster;
it's easy to teach - the teaching workload is smaller than that needed by other languages; this means that the teacher can put more emphasis on general (language-independent) programming techniques, not wasting energy on exotic tricks, strange exceptions and incomprehensible rules;
it's easy to use for writing new software - it's often possible to write code faster when using Python;
it's easy to understand - it's also often easier to understand someone else's code faster if it is written in Python;
it's easy to obtain, install and deploy - Python is free, open and multiplatform; not all languages can boast that.
Python rivals
Python has two direct competitors, with comparable properties and predispositions. These are:
Perl - a scripting language originally authored by Larry Wall;
Ruby - a scripting language originally authored by Yukihiro Matsumoto.
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Todos los ejemplos de código que encontrarás durante el curso se han probado con Python 3.4, Python 3.6 y Python 3.7.