This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, and features such as rapid development, object orientation, embedding in C, dynamic loading of modules, universal objects, and built-in interfaces to external services. The document also covers Python basics like data types, control flow, functions, modules, and exceptions. It provides examples of Python code and describes how to use Python in areas like shell tools, system administration, GUIs, databases, and distributed programming.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and philosophy of being coherent, powerful, and easy to read and maintain. Key features of Python mentioned include rapid development, object orientation, embedding in C, dynamic typing, exceptions, and built-in interfaces to external services. The document also outlines some common uses of Python and examples of basic Python code structure, variables, operations, control flow, functions, and data types like lists, tuples, and dictionaries.
This document provides an overview of the Python programming language. It discusses Python's history and origins, philosophy of being readable and powerful, features like dynamic typing and automatic memory management, uses for shell tools, prototyping, GUIs and more. It also covers Python syntax, modules, functions, control flow, objects and data types like lists, dictionaries and tuples.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and influences from other languages. Key features of Python mentioned include its rapid development cycle, automatic memory management, object-oriented programming support, and ability to be embedded in C/C++. The document also covers Python's basic syntax and data structures like lists, tuples, and dictionaries. It provides examples of control flow, functions, lambda forms, and list/dictionary methods.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points include that Python is an interpreted, object-oriented scripting language designed for readability and rapid development. It has automatic memory management, high-level data types, and built-in interfaces for tasks like GUI development. The document also covers Python programming basics like modules, functions, control flow, and data structures like lists, tuples, and dictionaries.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins in 1991 and heritage from languages like ABC, Tcl, and Perl. The document outlines Python's philosophy of coherence, power, and rapid development. Key Python features are summarized, including no compiling, dynamic typing, automatic memory management, and support for object-oriented, functional, and procedural programming. Example uses of Python like shell tools, system administration, GUIs, and web development are provided. The document also covers basic Python concepts like modules, statements, control flow, functions, strings, lists, dictionaries, and tuples.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and influences from other languages. Key features of Python mentioned include its rapid development cycle, automatic memory management, object-oriented programming support, and ability to be embedded in C/C++. The document also gives examples of common Python constructs like functions, control flow, lists, dictionaries, and modules.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from teaching languages. Key Python features include rapid development without compiling, automatic memory management, high-level data types, object-oriented programming, and embedding in C. The document also covers Python syntax, basic programming constructs like functions and control flow, data structures like lists and dictionaries, and functional programming tools.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and philosophy of being readable, powerful, and allowing for rapid development. Key Python features highlighted include dynamic typing, automatic memory management, object-oriented programming, and extensive standard libraries. The document also provides examples of basic Python syntax like variables, strings, lists, functions, control flow, and dictionaries.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and philosophy of being coherent, powerful, and easy to read and maintain. Key features of Python mentioned include rapid development, object orientation, embedding in C, dynamic typing, exceptions, and built-in interfaces to external services. The document also outlines some common uses of Python and examples of basic Python code structure, variables, operations, control flow, functions, and data types like lists, tuples, and dictionaries.
This document provides an overview of the Python programming language. It discusses Python's history and origins, philosophy of being readable and powerful, features like dynamic typing and automatic memory management, uses for shell tools, prototyping, GUIs and more. It also covers Python syntax, modules, functions, control flow, objects and data types like lists, dictionaries and tuples.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and influences from other languages. Key features of Python mentioned include its rapid development cycle, automatic memory management, object-oriented programming support, and ability to be embedded in C/C++. The document also covers Python's basic syntax and data structures like lists, tuples, and dictionaries. It provides examples of control flow, functions, lambda forms, and list/dictionary methods.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points include that Python is an interpreted, object-oriented scripting language designed for readability and rapid development. It has automatic memory management, high-level data types, and built-in interfaces for tasks like GUI development. The document also covers Python programming basics like modules, functions, control flow, and data structures like lists, tuples, and dictionaries.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins in 1991 and heritage from languages like ABC, Tcl, and Perl. The document outlines Python's philosophy of coherence, power, and rapid development. Key Python features are summarized, including no compiling, dynamic typing, automatic memory management, and support for object-oriented, functional, and procedural programming. Example uses of Python like shell tools, system administration, GUIs, and web development are provided. The document also covers basic Python concepts like modules, statements, control flow, functions, strings, lists, dictionaries, and tuples.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and influences from other languages. Key features of Python mentioned include its rapid development cycle, automatic memory management, object-oriented programming support, and ability to be embedded in C/C++. The document also gives examples of common Python constructs like functions, control flow, lists, dictionaries, and modules.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from teaching languages. Key Python features include rapid development without compiling, automatic memory management, high-level data types, object-oriented programming, and embedding in C. The document also covers Python syntax, basic programming constructs like functions and control flow, data structures like lists and dictionaries, and functional programming tools.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and philosophy of being readable, powerful, and allowing for rapid development. Key Python features highlighted include dynamic typing, automatic memory management, object-oriented programming, and extensive standard libraries. The document also provides examples of basic Python syntax like variables, strings, lists, functions, control flow, and dictionaries.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points include that Python is an interpreted, object-oriented scripting language designed for readability. It has automatic memory management, high-level data types, and support for procedural, object-oriented, and functional programming. Python can be used for tasks like shell scripting, system administration, rapid prototyping, web development, and more.
This document provides an overview of the Python programming language as presented in an advanced programming course at Columbia University in Spring 2002. It discusses Python's history and philosophy, features such as dynamic typing and memory management, basic syntax and programming constructs, functions, modules, and other language elements. The document is intended to introduce students to Python and provide an overview of its capabilities.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from languages like ABC and Tcl. Key features mentioned include rapid development cycle without compiling, automatic memory management, object-oriented programming, and embedding in C. The document also covers Python basics like data types, control flow, functions, modules, and lists/dictionaries. Common uses of Python include shell tools, system administration, rapid prototyping, and graphical user interfaces.
This document provides an overview of the Python programming language. It discusses that Python is a popular, object-oriented scripting language that emphasizes code readability. The document summarizes key Python features such as rapid development, automatic memory management, object-oriented programming, and embedding/extending with C. It also outlines common uses of Python and when it may not be suitable.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from other languages like ABC and Tcl. Key features mentioned include Python being an object-oriented language, its readability, power for both rapid development and large systems, integration capabilities, and elements borrowed from other languages. Various applications of Python like shell tools, extensions, GUI development, and scripting are also listed.
Python classes in mumbai
best Python classes in mumbai with job assistance.
our features are:
expert guidance by it industry professionals
lowest fees of 5000
practical exposure to handle projects
well equiped lab
after course resume writing guidance
This document provides an introduction and overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points covered include Python's simplicity, power, object-oriented approach, and wide portability. Examples are provided of basic Python syntax and constructs like strings, lists, functions, modules, and dictionaries.
This document provides an introduction to the basics of R programming. It begins with quizzes to assess the reader's familiarity with R and related topics. It then covers key R concepts like data types, data structures, importing and exporting data, control flow, functions, and parallel computing. The document aims to equip readers with fundamental R skills and directs them to online resources for further learning.
This document provides an introduction and overview of the Python programming language. It describes Python's origins, philosophy, features, and uses. Key points covered include Python's support for rapid development, object-oriented programming, embedding and extending with C, and its portability across platforms. Examples of Python code are provided to illustrate concepts like modules, functions, control flow, and data types.
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
This document provides an overview of the basics of R including why R is used, tutorials and links for learning R, an overview of the R interface and workspace, and how to get help in R. It discusses that R is a free and open-source statistical programming language used for statistical analysis and graphics. It has a steep learning curve due to the interactive nature of analyzing data through chained commands rather than single procedures. Help is provided through a built-in system and various online tutorials.
Modeling in R Programming Language for Beginers.pptanshikagoel52
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
R is a language and environment for statistical computing and graphics. It is based on S, an earlier language developed at Bell Labs. R features include being cross-platform, open source, having a package-based repository, strong graphics capabilities, and active user and developer communities. Useful URLs and books for learning R are provided. Instructions for installing R and RStudio on different platforms are given. R can be used for a wide range of statistical analyses and data visualization.
R is a software package for data analysis and graphical representation. It provides functions, results of analysis as objects, and a flexible environment for model building. This document provides tutorials on basic R operations including computation, vectors, matrices, and graphics. Key functions introduced are cbind(), rbind(), seq(), rep(), and matrix() for creating and manipulating objects, and plot() for data visualization.
This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).
Building High-Performance Language Implementations With Low EffortStefan Marr
This talk shows how languages can be implemented as self-optimizing interpreters, and how Truffle or RPython go about to just-in-time compile these interpreters to efficient native code.
Programming languages are never perfect, so people start building domain-specific languages to be able to solve their problems more easily. However, custom languages are often slow, or take enormous amounts of effort to be made fast by building custom compilers or virtual machines.
With the notion of self-optimizing interpreters, researchers proposed a way to implement languages easily and generate a JIT compiler from a simple interpreter. We explore the idea and experiment with it on top of RPython (of PyPy fame) with its meta-tracing JIT compiler, as well as Truffle, the JVM framework of Oracle Labs for self-optimizing interpreters.
In this talk, we show how a simple interpreter can reach the same order of magnitude of performance as the highly optimizing JVM for Java. We discuss the implementation on top of RPython as well as on top of Java with Truffle so that you can start right away, independent of whether you prefer the Python or JVM ecosystem.
While our own experiments focus on SOM, a little Smalltalk variant to keep things simple, other people have used this approach to improve peek performance of JRuby, or build languages such as JavaScript, R, and Python 3.
This document provides an overview of C# programming basics including:
1. Design environments like Visual Studio for creating C# projects
2. Key C# language components like variables, character constants, arithmetic operators
3. Examples of simple C# programs that demonstrate using the above components
4. Exercises for readers to practice creating C# programs using menus, numbers, etc.
Parallel R in snow (english after 2nd slide)Cdiscount
This presentation discusses parallelizing computations in R using the snow package. It demonstrates how to:
1. Create a cluster with multiple R sessions using makeCluster()
2. Split data across the sessions using clusterSplit() and export data to each node
3. Write functions to execute in parallel on each node using clusterEvalQ()
4. Collect the results, such as by summing outputs, to obtain the final parallelized computation. As an example, it shows how to parallelize the likelihood calculation for a probit regression model, reducing the computation time.
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
XP 2024 presentation: A New Look to Leadershipsamililja
Presentation slides from XP2024 conference, Bolzano IT. The slides describe a new view to leadership and combines it with anthro-complexity (aka cynefin).
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points include that Python is an interpreted, object-oriented scripting language designed for readability. It has automatic memory management, high-level data types, and support for procedural, object-oriented, and functional programming. Python can be used for tasks like shell scripting, system administration, rapid prototyping, web development, and more.
This document provides an overview of the Python programming language as presented in an advanced programming course at Columbia University in Spring 2002. It discusses Python's history and philosophy, features such as dynamic typing and memory management, basic syntax and programming constructs, functions, modules, and other language elements. The document is intended to introduce students to Python and provide an overview of its capabilities.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from languages like ABC and Tcl. Key features mentioned include rapid development cycle without compiling, automatic memory management, object-oriented programming, and embedding in C. The document also covers Python basics like data types, control flow, functions, modules, and lists/dictionaries. Common uses of Python include shell tools, system administration, rapid prototyping, and graphical user interfaces.
This document provides an overview of the Python programming language. It discusses that Python is a popular, object-oriented scripting language that emphasizes code readability. The document summarizes key Python features such as rapid development, automatic memory management, object-oriented programming, and embedding/extending with C. It also outlines common uses of Python and when it may not be suitable.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from other languages like ABC and Tcl. Key features mentioned include Python being an object-oriented language, its readability, power for both rapid development and large systems, integration capabilities, and elements borrowed from other languages. Various applications of Python like shell tools, extensions, GUI development, and scripting are also listed.
Python classes in mumbai
best Python classes in mumbai with job assistance.
our features are:
expert guidance by it industry professionals
lowest fees of 5000
practical exposure to handle projects
well equiped lab
after course resume writing guidance
This document provides an introduction and overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points covered include Python's simplicity, power, object-oriented approach, and wide portability. Examples are provided of basic Python syntax and constructs like strings, lists, functions, modules, and dictionaries.
This document provides an introduction to the basics of R programming. It begins with quizzes to assess the reader's familiarity with R and related topics. It then covers key R concepts like data types, data structures, importing and exporting data, control flow, functions, and parallel computing. The document aims to equip readers with fundamental R skills and directs them to online resources for further learning.
This document provides an introduction and overview of the Python programming language. It describes Python's origins, philosophy, features, and uses. Key points covered include Python's support for rapid development, object-oriented programming, embedding and extending with C, and its portability across platforms. Examples of Python code are provided to illustrate concepts like modules, functions, control flow, and data types.
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
This document provides an overview of the basics of R including why R is used, tutorials and links for learning R, an overview of the R interface and workspace, and how to get help in R. It discusses that R is a free and open-source statistical programming language used for statistical analysis and graphics. It has a steep learning curve due to the interactive nature of analyzing data through chained commands rather than single procedures. Help is provided through a built-in system and various online tutorials.
Modeling in R Programming Language for Beginers.pptanshikagoel52
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
R is a language and environment for statistical computing and graphics. It is based on S, an earlier language developed at Bell Labs. R features include being cross-platform, open source, having a package-based repository, strong graphics capabilities, and active user and developer communities. Useful URLs and books for learning R are provided. Instructions for installing R and RStudio on different platforms are given. R can be used for a wide range of statistical analyses and data visualization.
R is a software package for data analysis and graphical representation. It provides functions, results of analysis as objects, and a flexible environment for model building. This document provides tutorials on basic R operations including computation, vectors, matrices, and graphics. Key functions introduced are cbind(), rbind(), seq(), rep(), and matrix() for creating and manipulating objects, and plot() for data visualization.
This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).
Building High-Performance Language Implementations With Low EffortStefan Marr
This talk shows how languages can be implemented as self-optimizing interpreters, and how Truffle or RPython go about to just-in-time compile these interpreters to efficient native code.
Programming languages are never perfect, so people start building domain-specific languages to be able to solve their problems more easily. However, custom languages are often slow, or take enormous amounts of effort to be made fast by building custom compilers or virtual machines.
With the notion of self-optimizing interpreters, researchers proposed a way to implement languages easily and generate a JIT compiler from a simple interpreter. We explore the idea and experiment with it on top of RPython (of PyPy fame) with its meta-tracing JIT compiler, as well as Truffle, the JVM framework of Oracle Labs for self-optimizing interpreters.
In this talk, we show how a simple interpreter can reach the same order of magnitude of performance as the highly optimizing JVM for Java. We discuss the implementation on top of RPython as well as on top of Java with Truffle so that you can start right away, independent of whether you prefer the Python or JVM ecosystem.
While our own experiments focus on SOM, a little Smalltalk variant to keep things simple, other people have used this approach to improve peek performance of JRuby, or build languages such as JavaScript, R, and Python 3.
This document provides an overview of C# programming basics including:
1. Design environments like Visual Studio for creating C# projects
2. Key C# language components like variables, character constants, arithmetic operators
3. Examples of simple C# programs that demonstrate using the above components
4. Exercises for readers to practice creating C# programs using menus, numbers, etc.
Parallel R in snow (english after 2nd slide)Cdiscount
This presentation discusses parallelizing computations in R using the snow package. It demonstrates how to:
1. Create a cluster with multiple R sessions using makeCluster()
2. Split data across the sessions using clusterSplit() and export data to each node
3. Write functions to execute in parallel on each node using clusterEvalQ()
4. Collect the results, such as by summing outputs, to obtain the final parallelized computation. As an example, it shows how to parallelize the likelihood calculation for a probit regression model, reducing the computation time.
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
XP 2024 presentation: A New Look to Leadershipsamililja
Presentation slides from XP2024 conference, Bolzano IT. The slides describe a new view to leadership and combines it with anthro-complexity (aka cynefin).
This presentation by Thibault Schrepel, Associate Professor of Law at Vrije Universiteit Amsterdam University, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
Mastering the Concepts Tested in the Databricks Certified Data Engineer Assoc...SkillCertProExams
• For a full set of 760+ questions. Go to
https://skillcertpro.com/product/databricks-certified-data-engineer-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
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This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Collapsing Narratives: Exploring Non-Linearity • a micro report by Rosie WellsRosie Wells
Insight: In a landscape where traditional narrative structures are giving way to fragmented and non-linear forms of storytelling, there lies immense potential for creativity and exploration.
'Collapsing Narratives: Exploring Non-Linearity' is a micro report from Rosie Wells.
Rosie Wells is an Arts & Cultural Strategist uniquely positioned at the intersection of grassroots and mainstream storytelling.
Their work is focused on developing meaningful and lasting connections that can drive social change.
Please download this presentation to enjoy the hyperlinks!
Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...Suzanne Lagerweij
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
Carrer goals.pptx and their importance in real lifeartemacademy2
Career goals serve as a roadmap for individuals, guiding them toward achieving long-term professional aspirations and personal fulfillment. Establishing clear career goals enables professionals to focus their efforts on developing specific skills, gaining relevant experience, and making strategic decisions that align with their desired career trajectory. By setting both short-term and long-term objectives, individuals can systematically track their progress, make necessary adjustments, and stay motivated. Short-term goals often include acquiring new qualifications, mastering particular competencies, or securing a specific role, while long-term goals might encompass reaching executive positions, becoming industry experts, or launching entrepreneurial ventures.
Moreover, having well-defined career goals fosters a sense of purpose and direction, enhancing job satisfaction and overall productivity. It encourages continuous learning and adaptation, as professionals remain attuned to industry trends and evolving job market demands. Career goals also facilitate better time management and resource allocation, as individuals prioritize tasks and opportunities that advance their professional growth. In addition, articulating career goals can aid in networking and mentorship, as it allows individuals to communicate their aspirations clearly to potential mentors, colleagues, and employers, thereby opening doors to valuable guidance and support. Ultimately, career goals are integral to personal and professional development, driving individuals toward sustained success and fulfillment in their chosen fields.
This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdfBen Linders
Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.
Mẫu PPT kế hoạch làm việc sáng tạo cho nửa cuối năm PowerPoint
python.ppt
1. 15-Nov-22 Advanced Programming
Spring 2002
Python
Henning Schulzrinne
Department of Computer Science
Columbia University
(based on tutorial by Guido van Rossum)
2. 15-Nov-22 Advanced Programming
Spring 2002
Introduction
Most recent popular
(scripting/extension) language
although origin ~1991
heritage: teaching language (ABC)
Tcl: shell
perl: string (regex) processing
object-oriented
rather than add-on (OOTcl)
3. 15-Nov-22 Advanced Programming
Spring 2002
Python philosophy
Coherence
not hard to read, write and maintain
power
scope
rapid development + large systems
objects
integration
hybrid systems
4. 15-Nov-22 Advanced Programming
Spring 2002
Python features
no compiling or linking rapid development cycle
no type declarations simpler, shorter, more flexible
automatic memory management garbage collection
high-level data types and
operations
fast development
object-oriented programming code structuring and reuse, C++
embedding and extending in C mixed language systems
classes, modules, exceptions "programming-in-the-large"
support
dynamic loading of C modules simplified extensions, smaller
binaries
dynamic reloading of C modules programs can be modified without
stopping
Lutz, Programming Python
5. 15-Nov-22 Advanced Programming
Spring 2002
Python features
universal "first-class" object model fewer restrictions and rules
run-time program construction handles unforeseen needs, end-
user coding
interactive, dynamic nature incremental development and
testing
access to interpreter information metaprogramming, introspective
objects
wide portability cross-platform programming
without ports
compilation to portable byte-code execution speed, protecting source
code
built-in interfaces to external
services
system tools, GUIs, persistence,
databases, etc.
Lutz, Programming Python
6. 15-Nov-22 Advanced Programming
Spring 2002
Python
elements from C++, Modula-3
(modules), ABC, Icon (slicing)
same family as Perl, Tcl, Scheme, REXX,
BASIC dialects
7. 15-Nov-22 Advanced Programming
Spring 2002
Uses of Python
shell tools
system admin tools, command line programs
extension-language work
rapid prototyping and development
language-based modules
instead of special-purpose parsers
graphical user interfaces
database access
distributed programming
Internet scripting
8. 15-Nov-22 Advanced Programming
Spring 2002
What not to use Python (and
kin) for
most scripting languages share these
not as efficient as C
but sometimes better built-in algorithms
(e.g., hashing and sorting)
delayed error notification
lack of profiling tools
9. 15-Nov-22 Advanced Programming
Spring 2002
Using python
/usr/local/bin/python
#! /usr/bin/env python
interactive use
Python 1.6 (#1, Sep 24 2000, 20:40:45) [GCC 2.95.1 19990816 (release)] on sunos5
Copyright (c) 1995-2000 Corporation for National Research Initiatives.
All Rights Reserved.
Copyright (c) 1991-1995 Stichting Mathematisch Centrum, Amsterdam.
All Rights Reserved.
>>>
python –c command [arg] ...
python –i script
read script first, then interactive
10. 15-Nov-22 Advanced Programming
Spring 2002
Python structure
modules: Python source files or C extensions
import, top-level via from, reload
statements
control flow
create objects
indentation matters – instead of {}
objects
everything is an object
automatically reclaimed when no longer needed
11. 15-Nov-22 Advanced Programming
Spring 2002
First example
#!/usr/local/bin/python
# import systems module
import sys
marker = '::::::'
for name in sys.argv[1:]:
input = open(name, 'r')
print marker + name
print input.read()
12. 15-Nov-22 Advanced Programming
Spring 2002
Basic operations
Assignment:
size = 40
a = b = c = 3
Numbers
integer, float
complex numbers: 1j+3, abs(z)
Strings
'hello world', 'it's hot'
"bye world"
continuation via or use """ long text """"
13. 15-Nov-22 Advanced Programming
Spring 2002
String operations
concatenate with + or neighbors
word = 'Help' + x
word = 'Help' 'a'
subscripting of strings
'Hello'[2] 'l'
slice: 'Hello'[1:2] 'el'
word[-1] last character
len(word) 5
immutable: cannot assign to subscript
14. 15-Nov-22 Advanced Programming
Spring 2002
Lists
lists can be heterogeneous
a = ['spam', 'eggs', 100, 1234, 2*2]
Lists can be indexed and sliced:
a[0] spam
a[:2] ['spam', 'eggs']
Lists can be manipulated
a[2] = a[2] + 23
a[0:2] = [1,12]
a[0:0] = []
len(a) 5
15. 15-Nov-22 Advanced Programming
Spring 2002
Basic programming
a,b = 0, 1
# non-zero = true
while b < 10:
# formatted output, without n
print b,
# multiple assignment
a,b = b, a+b
16. 15-Nov-22 Advanced Programming
Spring 2002
Control flow: if
x = int(raw_input("Please enter #:"))
if x < 0:
x = 0
print 'Negative changed to zero'
elif x == 0:
print 'Zero'
elif x == 1:
print 'Single'
else:
print 'More'
no case statement
17. 15-Nov-22 Advanced Programming
Spring 2002
Control flow: for
a = ['cat', 'window', 'defenestrate']
for x in a:
print x, len(x)
no arithmetic progression, but
range(10) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
for i in range(len(a)):
print i, a[i]
do not modify the sequence being iterated
over
18. 15-Nov-22 Advanced Programming
Spring 2002
Loops: break, continue, else
break and continue like C
else after loop exhaustion
for n in range(2,10):
for x in range(2,n):
if n % x == 0:
print n, 'equals', x, '*', n/x
break
else:
# loop fell through without finding a factor
print n, 'is prime'
20. 15-Nov-22 Advanced Programming
Spring 2002
Defining functions
def fib(n):
"""Print a Fibonacci series up to n."""
a, b = 0, 1
while b < n:
print b,
a, b = b, a+b
>>> fib(2000)
First line is docstring
first look for variables in local, then global
need global to assign global variables
21. 15-Nov-22 Advanced Programming
Spring 2002
Functions: default argument
values
def ask_ok(prompt, retries=4,
complaint='Yes or no, please!'):
while 1:
ok = raw_input(prompt)
if ok in ('y', 'ye', 'yes'): return 1
if ok in ('n', 'no'): return 0
retries = retries - 1
if retries < 0: raise IOError,
'refusenik error'
print complaint
>>> ask_ok('Really?')
22. 15-Nov-22 Advanced Programming
Spring 2002
Keyword arguments
last arguments can be given as keywords
def parrot(voltage, state='a stiff', action='voom',
type='Norwegian blue'):
print "-- This parrot wouldn't", action,
print "if you put", voltage, "Volts through it."
print "Lovely plumage, the ", type
print "-- It's", state, "!"
parrot(1000)
parrot(action='VOOOM', voltage=100000)
23. 15-Nov-22 Advanced Programming
Spring 2002
Lambda forms
anonymous functions
may not work in older versions
def make_incrementor(n):
return lambda x: x + n
f = make_incrementor(42)
f(0)
f(1)
24. 15-Nov-22 Advanced Programming
Spring 2002
List methods
append(x)
extend(L)
append all items in list (like Tcl lappend)
insert(i,x)
remove(x)
pop([i]), pop()
create stack (FIFO), or queue (LIFO) pop(0)
index(x)
return the index for value x
25. 15-Nov-22 Advanced Programming
Spring 2002
List methods
count(x)
how many times x appears in list
sort()
sort items in place
reverse()
reverse list
26. 15-Nov-22 Advanced Programming
Spring 2002
Functional programming tools
filter(function, sequence)
def f(x): return x%2 != 0 and x%3 0
filter(f, range(2,25))
map(function, sequence)
call function for each item
return list of return values
reduce(function, sequence)
return a single value
call binary function on the first two items
then on the result and next item
iterate
27. 15-Nov-22 Advanced Programming
Spring 2002
List comprehensions (2.0)
Create lists without map(),
filter(), lambda
= expression followed by for clause +
zero or more for or of clauses
>>> vec = [2,4,6]
>>> [3*x for x in vec]
[6, 12, 18]
>>> [{x: x**2} for x in vec}
[{2: 4}, {4: 16}, {6: 36}]
28. 15-Nov-22 Advanced Programming
Spring 2002
List comprehensions
cross products:
>>> vec1 = [2,4,6]
>>> vec2 = [4,3,-9]
>>> [x*y for x in vec1 for y in vec2]
[8,6,-18, 16,12,-36, 24,18,-54]
>>> [x+y for x in vec1 and y in vec2]
[6,5,-7,8,7,-5,10,9,-3]
>>> [vec1[i]*vec2[i] for i in
range(len(vec1))]
[8,12,-54]
29. 15-Nov-22 Advanced Programming
Spring 2002
List comprehensions
can also use if:
>>> [3*x for x in vec if x > 3]
[12, 18]
>>> [3*x for x in vec if x < 2]
[]
30. 15-Nov-22 Advanced Programming
Spring 2002
del – removing list items
remove by index, not value
remove slices from list (rather than by
assigning an empty list)
>>> a = [-1,1,66.6,333,333,1234.5]
>>> del a[0]
>>> a
[1,66.6,333,333,1234.5]
>>> del a[2:4]
>>> a
[1,66.6,1234.5]
31. 15-Nov-22 Advanced Programming
Spring 2002
Tuples and sequences
lists, strings, tuples: examples of
sequence type
tuple = values separated by commas
>>> t = 123, 543, 'bar'
>>> t[0]
123
>>> t
(123, 543, 'bar')
32. 15-Nov-22 Advanced Programming
Spring 2002
Tuples
Tuples may be nested
>>> u = t, (1,2)
>>> u
((123, 542, 'bar'), (1,2))
kind of like structs, but no element names:
(x,y) coordinates
database records
like strings, immutable can't assign to
individual items
34. 15-Nov-22 Advanced Programming
Spring 2002
Tuples
sequence unpacking distribute
elements across variables
>>> t = 123, 543, 'bar'
>>> x, y, z = t
>>> x
123
packing always creates tuple
unpacking works for any sequence
35. 15-Nov-22 Advanced Programming
Spring 2002
Dictionaries
like Tcl or awk associative arrays
indexed by keys
keys are any immutable type: e.g., tuples
but not lists (mutable!)
uses 'key: value' notation
>>> tel = {'hgs' : 7042, 'lennox': 7018}
>>> tel['cs'] = 7000
>>> tel
36. 15-Nov-22 Advanced Programming
Spring 2002
Dictionaries
no particular order
delete elements with del
>>> del tel['foo']
keys() method unsorted list of keys
>>> tel.keys()
['cs', 'lennox', 'hgs']
use has_key() to check for existence
>>> tel.has_key('foo')
0
37. 15-Nov-22 Advanced Programming
Spring 2002
Conditions
can check for sequence membership with is
and is not:
>>> if (4 in vec):
... print '4 is'
chained comparisons: a less than b AND b
equals c:
a < b == c
and and or are short-circuit operators:
evaluated from left to right
stop evaluation as soon as outcome clear
38. 15-Nov-22 Advanced Programming
Spring 2002
Conditions
Can assign comparison to variable:
>>> s1,s2,s3='', 'foo', 'bar'
>>> non_null = s1 or s2 or s3
>>> non_null
foo
Unlike C, no assignment within
expression
39. 15-Nov-22 Advanced Programming
Spring 2002
Comparing sequences
unlike C, can compare sequences (lists,
tuples, ...)
lexicographical comparison:
compare first; if different outcome
continue recursively
subsequences are smaller
strings use ASCII comparison
can compare objects of different type, but
by type name (list < string < tuple)
41. 15-Nov-22 Advanced Programming
Spring 2002
Modules
collection of functions and variables,
typically in scripts
definitions can be imported
file name is module name + .py
e.g., create module fibo.py
def fib(n): # write Fib. series up to n
...
def fib2(n): # return Fib. series up to n
42. 15-Nov-22 Advanced Programming
Spring 2002
Modules
import module:
import fibo
Use modules via "name space":
>>> fibo.fib(1000)
>>> fibo.__name__
'fibo'
can give it a local name:
>>> fib = fibo.fib
>>> fib(500)
43. 15-Nov-22 Advanced Programming
Spring 2002
Modules
function definition + executable statements
executed only when module is imported
modules have private symbol tables
avoids name clash for global variables
accessible as module.globalname
can import into name space:
>>> from fibo import fib, fib2
>>> fib(500)
can import all names defined by module:
>>> from fibo import *
44. 15-Nov-22 Advanced Programming
Spring 2002
Module search path
current directory
list of directories specified in PYTHONPATH
environment variable
uses installation-default if not defined, e.g.,
.:/usr/local/lib/python
uses sys.path
>>> import sys
>>> sys.path
['', 'C:PROGRA~1Python2.2', 'C:Program
FilesPython2.2DLLs', 'C:Program
FilesPython2.2lib', 'C:Program
FilesPython2.2liblib-tk', 'C:Program
FilesPython2.2', 'C:Program FilesPython2.2libsite-
packages']
45. 15-Nov-22 Advanced Programming
Spring 2002
Compiled Python files
include byte-compiled version of module if
there exists fibo.pyc in same directory as
fibo.py
only if creation time of fibo.pyc matches
fibo.py
automatically write compiled file, if possible
platform independent
doesn't run any faster, but loads faster
can have only .pyc file hide source
46. 15-Nov-22 Advanced Programming
Spring 2002
Standard modules
system-dependent list
always sys module
>>> import sys
>>> sys.p1
'>>> '
>>> sys.p2
'... '
>>> sys.path.append('/some/directory')
48. 15-Nov-22 Advanced Programming
Spring 2002
Classes
mixture of C++ and Modula-3
multiple base classes
derived class can override any methods of its
base class(es)
method can call the method of a base class
with the same name
objects have private data
C++ terms:
all class members are public
all member functions are virtual
no constructors or destructors (not needed)
49. 15-Nov-22 Advanced Programming
Spring 2002
Classes
classes (and data types) are objects
built-in types cannot be used as base
classes by user
arithmetic operators, subscripting can
be redefined for class instances (like
C++, unlike Java)
50. 15-Nov-22 Advanced Programming
Spring 2002
Class definitions
Class ClassName:
<statement-1>
...
<statement-N>
must be executed
can be executed conditionally (see Tcl)
creates new namespace
51. 15-Nov-22 Advanced Programming
Spring 2002
Namespaces
mapping from name to object:
built-in names (abs())
global names in module
local names in function invocation
attributes = any following a dot
z.real, z.imag
attributes read-only or writable
module attributes are writeable
52. 15-Nov-22 Advanced Programming
Spring 2002
Namespaces
scope = textual region of Python program
where a namespace is directly accessible
(without dot)
innermost scope (first) = local names
middle scope = current module's global names
outermost scope (last) = built-in names
assignments always affect innermost scope
don't copy, just create name bindings to objects
global indicates name is in global scope
53. 15-Nov-22 Advanced Programming
Spring 2002
Class objects
obj.name references (plus module!):
class MyClass:
"A simple example class"
i = 123
def f(self):
return 'hello world'
>>> MyClass.i
123
MyClass.f is method object
54. 15-Nov-22 Advanced Programming
Spring 2002
Class objects
class instantiation:
>>> x = MyClass()
>>> x.f()
'hello world'
creates new instance of class
note x = MyClass vs. x = MyClass()
___init__() special method for
initialization of object
def __init__(self,realpart,imagpart):
self.r = realpart
self.i = imagpart
55. 15-Nov-22 Advanced Programming
Spring 2002
Instance objects
attribute references
data attributes (C++/Java data
members)
created dynamically
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print x.counter
del x.counter
56. 15-Nov-22 Advanced Programming
Spring 2002
Method objects
Called immediately:
x.f()
can be referenced:
xf = x.f
while 1:
print xf()
object is passed as first argument of
function 'self'
x.f() is equivalent to MyClass.f(x)
57. 15-Nov-22 Advanced Programming
Spring 2002
Notes on classes
Data attributes override method
attributes with the same name
no real hiding not usable to
implement pure abstract data types
clients (users) of an object can add
data attributes
first argument of method usually called
self
'self' has no special meaning (cf. Java)
58. 15-Nov-22 Advanced Programming
Spring 2002
Another example
bag.py
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self,x):
self.add(x)
self.add(x)
59. 15-Nov-22 Advanced Programming
Spring 2002
Another example, cont'd.
invoke:
>>> from bag import *
>>> l = Bag()
>>> l.add('first')
>>> l.add('second')
>>> l.data
['first', 'second']
60. 15-Nov-22 Advanced Programming
Spring 2002
Inheritance
class DerivedClassName(BaseClassName)
<statement-1>
...
<statement-N>
search class attribute, descending chain
of base classes
may override methods in the base class
call directly via BaseClassName.method
61. 15-Nov-22 Advanced Programming
Spring 2002
Multiple inheritance
class DerivedClass(Base1,Base2,Base3):
<statement>
depth-first, left-to-right
problem: class derived from two classes
with a common base class
62. 15-Nov-22 Advanced Programming
Spring 2002
Private variables
No real support, but textual
replacement (name mangling)
__var is replaced by
_classname_var
prevents only accidental modification,
not true protection
63. 15-Nov-22 Advanced Programming
Spring 2002
~ C structs
Empty class definition:
class Employee:
pass
john = Employee()
john.name = 'John Doe'
john.dept = 'CS'
john.salary = 1000
64. 15-Nov-22 Advanced Programming
Spring 2002
Exceptions
syntax (parsing) errors
while 1 print 'Hello World'
File "<stdin>", line 1
while 1 print 'Hello World'
^
SyntaxError: invalid syntax
exceptions
run-time errors
e.g., ZeroDivisionError,
NameError, TypeError
65. 15-Nov-22 Advanced Programming
Spring 2002
Handling exceptions
while 1:
try:
x = int(raw_input("Please enter a number: "))
break
except ValueError:
print "Not a valid number"
First, execute try clause
if no exception, skip except clause
if exception, skip rest of try clause and use except
clause
if no matching exception, attempt outer try
statement
66. 15-Nov-22 Advanced Programming
Spring 2002
Handling exceptions
try.py
import sys
for arg in sys.argv[1:]:
try:
f = open(arg, 'r')
except IOError:
print 'cannot open', arg
else:
print arg, 'lines:', len(f.readlines())
f.close
e.g., as python try.py *.py