Four Python Pains
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Python has four main pain points: speed, scalability, type safety, and adherence to the CPython implementation. It discusses how Python could potentially be improved by applying techniques from other languages to increase speed, removing the global interpreter lock to improve scalability, using static type analysis tools and annotations to increase type safety, and developing higher-level tools for writing extensions to improve portability across implementations. Overall, the document analyzes potential issues with the Python language and explores possible solutions.
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- 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,
An Introduction to PyPy
The core idea of PyPy is to produce a flexible and fast implementation of the Python programming language. The talk will cover the interpreter, translator and jit parts of the code and their relationships and the fundamental ways in which PyPy differs from other virtual machine implementations.
What is python
Python is an easy to learn programming language that is widely used for a variety of tasks. It has a simple syntax that allows developers to focus on solving problems rather than dealing with complex language features. Python code can be written quickly and read easily by others. It also has a large ecosystem of libraries and frameworks that support application development, data science, machine learning, and more. While not the fastest language, Python makes up for it with versatility and the ability to connect different systems through its "glue" programming capabilities.
Introduction to Python Programming Basics
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.
Python Programming1.ppt
Python was conceived in the late 1980s and its implementation was started in 1989. Python 2.0 was released in 2000 with major new features like garbage collection and unicode support. Python 3.0 was released in 2008 as a backwards-incompatible version after long testing. Python is a general-purpose, high-level programming language designed for code readability. It supports multiple programming paradigms like object-oriented, imperative, and functional programming. Python has a large standard library and interpreters available for many operating systems.
IPT 2.pptx
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.
Python.pptx
The document provides an overview of the history and key features of the Python programming language. It discusses that Python was created in 1989 by Guido van Rossum as an easy to use scripting language. It gained popularity through its open source model and large standard library. Key features include being easy to learn, having a clear readable syntax, being interpreted, object-oriented, cross-platform, and having a vast library of functions. Popular applications of Python include GUI programs, web development, science, education, and more.
2015 bioinformatics python_introduction_wim_vancriekinge_vfinal
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.
Python programming ppt.pptx
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
PYTHON UNIT 1
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
Python programming
Python is a general purpose, dynamic, high-level and interpreted programming language. It is used widely in data science, machine learning, web development, automation and more. Python was created in the 1990s by Guido van Rossum to be an interpreted language that bridged the gap between C and shell scripting. It has many advantages like being readable, cross-platform, having a large standard library and being open source.
Python Programming
This document introduces the Python programming language. It discusses that Python is an interpreted, high-level, general-purpose and dynamic programming language that is free and open-source. It requires fewer lines of code than languages like C++ and Java. Python supports features like object-oriented, functional and procedural programming. It has a large standard library and can be used across operating systems. The document also describes IDLE as an integrated development environment for Python and covers basic Python concepts like comments, identifiers and the interactive shell.
Python Programming Language
Python is a widely used general purpose programming language that was created in the late 1980s by Guido van Rossum. It emphasizes code readability and has a large standard library. It supports multiple programming paradigms like object oriented, imperative, and functional programming. Compared to other languages, Python programs are typically shorter than equivalent programs in languages like Java due to features like dynamic typing.
Introduction to python
Python is a high-level programming language that emphasizes code readability. It has a clear syntax and large standard library. Python can be used for system programming, GUIs, internet scripting, database programming, and more. Some key strengths of Python include being object-oriented, free, portable, powerful, easy to use and learn. Popular uses of Python include web development, scientific computing, and financial applications. The document provides an overview of Python fundamentals like data types, control flow statements, functions, classes, and modules.
Introduction to Jupyter notebook and MS Azure Machine Learning Studio
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.
Introduction to Jupyter notebook and MS Azure Machine Learning Studio
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.
Python 101 for the .NET Developer
This document provides an introduction to Python for .NET developers. Python is an interpreted, object-oriented programming language that is portable, extensible, and combines power with clear syntax. It differs from .NET in that it uses dynamic typing, whitespace is significant, and functions must be defined before use. Python code is often organized into modules stored in files with a .py extension. The document discusses Python implementations, modules, strengths, weaknesses and success stories. It also introduces the IronPython implementation and some Python web frameworks.
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Four Python Pains
- 1. Four Python Pains Stefane Fermigier Scripting Languages Workshop, IRILL, May 2011
- 11. Python (“Scripting Language”) • Has built-in syntactical support for arbitrarily complex data structures (lists, dictionaries) • Supports significantly complex applications: ERP5, Plone, Nuxeo CPS (~320 KLOC), OpenERP (~150 KLOC), Komodo (~250 KLOC) • Is compiled (to bytecode)
- 12. Scala (“Sys. Prog. Language”) • Is interpreted (it as a REPL) • Is a high-level language - in other words, has a high CPU instructions / statement ratio (comparable or higher than Python)
- 13. Other counterexamples • Java, pre-generics (Java SE < 5, i.e. at the time of Ousterhout’s paper), had really weak support for static typing • Clojure (that many consider in the same league as Scala) is not even statically typed
- 17. • Execution speed? • Safety? • Maintainability? • What else?
- 18. Or maybe Python is a scripting language after all ?
- 19. Uses of Python • Education • Small to medium sized sysadmin and software engineering project • Medium to large web frameworks (Zope, Django...) and applications (Youtube...) • Scientific computing (numpy, scipy...) • Scripting of games, desktop apps, etc.
- 20. Recap: 4 pain points • Speed • Scalability • Type-safety • Adherence to external code bases, and an implementation (CPython)
- 22. Speed • Python (seems to be) slower by 10x than Clojure and V8 (JavaScript) • Both Clojure and JavaScript are dynamically typed languages • TODO: look at Clojure and V8 and see how they do it
- 24. Scalability • CPython multi-threading / multi-core performance is constrained by “the GIL” (global interpretor lock) • It doesn’t have to be there (Jython doesn’t have one) but removing it from Python is controversial • Python has support for some par prog constructs (see: http://wiki.python.org/moin/ ParallelProcessing) but not the fancy new ones (a la Scala, Clojure, Go, etc.)
- 26. Type Safety • Static typic is good to create safer programs, and also to ease refactoring (necessary condition for agility) • Since Python 3, type annotations are possible on variables, parameters, etc. • Some tools (e.g. Jetbrains’ PyCharm) have support for type inference on Python, and enable: code completion, errors detections, refactoring...
- 28. Adherence to CPython • There are at least 4 major Python implementations: CPython, Jython, IronPython and PyPy • But only CPython is mainstream • Why? Because all the “system programming lang” extensions written for CPython cannot be used with the others • And we’re talking about hundreds of packages
- 29. Solution? • There are also tools, languages and extensions that ease up the task of writing a CPython extension: SWIG, ctypes, Pyrex, Cython... • By encapsulating part of the complexity and low-level details of creating a Python extension, it could make is both easier, more robust and easier to share extensions between language implementations
- 30. Conclusion
- 31. • Python could maybe be made 10x faster by applying ideas from Clojure and V8 • Python could be made more scalable by removing the GIL (probably needs to rewrite the interpreter, though) and adding modern parallel programming paradigms
- 32. • Python programs could be made more robust with the help of static typing analysis tools + a little help from the developers (i.e. add a few type annotations in their programs and libraries) • Python is a scripting language after all (but not only), and could benefit from a way of writing extensions that is higher level than the common one (but then: how to convert the existing code base?)
Editor's Notes
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