This document provides an introduction to Python for high school programmers. It covers background information on Python, key concepts like data types and operators, and basics of the language like variables, collections, control flow, and object-oriented programming. Code examples are included to demonstrate various features. The presentation aims to get students started with Python and provide an overview of what it can do.
Simply Business is starting to look into new tools to improve some of our mission-critical systems. There is one application, which would hugely benefit from the concurrency and fault tolerance model offered by languages like Elixir.
To increase awareness and gauge interest in the technology, we will have a bootcamp dedicated to giving us more insights into how to build and architect applications using Elixir and OTP.
It is meant to aim for slightly more advanced concepts, so in order to prepare rest of the team to be able to read the code and have some basic understanding of constructs and tooling - we have organised a LevelUP session, to talk exactly about that...
Elixir & Phoenix – fast, concurrent and explicitTobias Pfeiffer
Elixir and Phoenix are known for their speed, but that’s far from their only benefit. Elixir isn’t just a fast Ruby and Phoenix isn’t just Rails for Elixir. Through pattern matching, immutable data structures and new idioms your programs can not only become faster but more understandable and maintainable. This talk will take a look at what’s great, what you might miss and augment it with production experience and advice.
Elixir & Phoenix – fast, concurrent and explicitTobias Pfeiffer
Elixir and Phoenix are known for their speed, but that’s far from their only benefit. Elixir isn’t just a fast Ruby and Phoenix isn’t just Rails for Elixir. Through pattern matching, immutable data structures and new idioms your programs can not only become faster but more understandable and maintainable. This talk will take a look at what’s great, what you might miss and augment it with production experience and advice.
Simply Business is starting to look into new tools to improve some of our mission-critical systems. There is one application, which would hugely benefit from the concurrency and fault tolerance model offered by languages like Elixir.
To increase awareness and gauge interest in the technology, we will have a bootcamp dedicated to giving us more insights into how to build and architect applications using Elixir and OTP.
It is meant to aim for slightly more advanced concepts, so in order to prepare rest of the team to be able to read the code and have some basic understanding of constructs and tooling - we have organised a LevelUP session, to talk exactly about that...
Elixir & Phoenix – fast, concurrent and explicitTobias Pfeiffer
Elixir and Phoenix are known for their speed, but that’s far from their only benefit. Elixir isn’t just a fast Ruby and Phoenix isn’t just Rails for Elixir. Through pattern matching, immutable data structures and new idioms your programs can not only become faster but more understandable and maintainable. This talk will take a look at what’s great, what you might miss and augment it with production experience and advice.
Elixir & Phoenix – fast, concurrent and explicitTobias Pfeiffer
Elixir and Phoenix are known for their speed, but that’s far from their only benefit. Elixir isn’t just a fast Ruby and Phoenix isn’t just Rails for Elixir. Through pattern matching, immutable data structures and new idioms your programs can not only become faster but more understandable and maintainable. This talk will take a look at what’s great, what you might miss and augment it with production experience and advice.
How fast ist it really? Benchmarking in practiceTobias Pfeiffer
“What’s the fastest way of doing this?” - you might ask yourself during development. Sure, you can guess what’s fastest or how long something will take, but do you know? How long does it take to sort a list of 1 Million elements? Are tail-recursive functions always the fastest?
Benchmarking is here to answer these questions. However, there are many pitfalls around setting up a good benchmark and interpreting the results. This talk will guide you through, introduce best practices and show you some surprising benchmarking results along the way.
Keynote presented at European Testing Conference (9th February 2017)
What happens when things break? What happens when software fails? We regard it as a normal and personal inconvenience when apps crash or servers become unavailable, but what are the implications beyond the individual user? Is software reliability simply a business decision or does it have economic, social and cultural consequences? What are the moral and practical implications for software developers? And when we talk of ‘systems’, are we part of the ‘system’? What about the bugs on our side of the keyboard? In this talk we will explore examples of failures in software and its application, and how they affect us at different scales, from user to society.
Functional Pe(a)rls - the Purely Functional Datastructures editionosfameron
All new material, this time about one of the fundamental functional datastructures, the Linked List, and the overview of an implementation in Moosey Perl.
This covers some of the same material as https://github.com/osfameron/pure-fp-book but perhaps with more explanation (and covering much less material - it was only a 20 minute talk)
Some techniques from the heady world of Functional Programming implemented in idiomatic Perl using various techniques: closures, iterators, Devel::Declare, and some distilled evil. New version now with monads! (As presented at http://conferences.yapceurope.org/lpw2008/ )
From session at http://www.lambdalounge.org.uk/ on 18th April 2016. Here's the original blurb:
So, Haskell is "an advanced purely-functional programming language" which supports writing "declarative, statically typed code". It may be optimized for academic buzzwords you've never heard of but... is it any good for writing code in the way that you'd write Perl, Python, or Ruby?
What are strong types, and why are we so frightened of them anyway? Can you develop interactively in Haskell, the way you would in a dynamic language?
Does Haskell have "whipuptitude" (being able to get things done quickly) as well as "manipulexity" (being able to manipulate complex things)? And perhaps most importantly, can writing Haskell be *fun*?
Haskell is founded on decades of the finest mathematical and computer science research. Perl, quite demonstrably isn't... but why do so many Perl programmers also love Haskell?
Audrey Tang wrote the first prototype for Perl 6, Pugs, in Haskell, and coined the phrase "lambdacamel" for the substantial crossover between the languages.
What does a Perl programmer make of Haskell? What are the lessons that can be learned (in either direction). And do the languages have more in common than you might have thought?
The basics of Python are rather straightforward. In a few minutes you can learn most of the syntax. There are some gotchas along the way that might appear tricky. This talk is meant to bring programmers up to speed with Python. They should be able to read and write Python.
How fast ist it really? Benchmarking in practiceTobias Pfeiffer
“What’s the fastest way of doing this?” - you might ask yourself during development. Sure, you can guess what’s fastest or how long something will take, but do you know? How long does it take to sort a list of 1 Million elements? Are tail-recursive functions always the fastest?
Benchmarking is here to answer these questions. However, there are many pitfalls around setting up a good benchmark and interpreting the results. This talk will guide you through, introduce best practices and show you some surprising benchmarking results along the way.
Keynote presented at European Testing Conference (9th February 2017)
What happens when things break? What happens when software fails? We regard it as a normal and personal inconvenience when apps crash or servers become unavailable, but what are the implications beyond the individual user? Is software reliability simply a business decision or does it have economic, social and cultural consequences? What are the moral and practical implications for software developers? And when we talk of ‘systems’, are we part of the ‘system’? What about the bugs on our side of the keyboard? In this talk we will explore examples of failures in software and its application, and how they affect us at different scales, from user to society.
Functional Pe(a)rls - the Purely Functional Datastructures editionosfameron
All new material, this time about one of the fundamental functional datastructures, the Linked List, and the overview of an implementation in Moosey Perl.
This covers some of the same material as https://github.com/osfameron/pure-fp-book but perhaps with more explanation (and covering much less material - it was only a 20 minute talk)
Some techniques from the heady world of Functional Programming implemented in idiomatic Perl using various techniques: closures, iterators, Devel::Declare, and some distilled evil. New version now with monads! (As presented at http://conferences.yapceurope.org/lpw2008/ )
From session at http://www.lambdalounge.org.uk/ on 18th April 2016. Here's the original blurb:
So, Haskell is "an advanced purely-functional programming language" which supports writing "declarative, statically typed code". It may be optimized for academic buzzwords you've never heard of but... is it any good for writing code in the way that you'd write Perl, Python, or Ruby?
What are strong types, and why are we so frightened of them anyway? Can you develop interactively in Haskell, the way you would in a dynamic language?
Does Haskell have "whipuptitude" (being able to get things done quickly) as well as "manipulexity" (being able to manipulate complex things)? And perhaps most importantly, can writing Haskell be *fun*?
Haskell is founded on decades of the finest mathematical and computer science research. Perl, quite demonstrably isn't... but why do so many Perl programmers also love Haskell?
Audrey Tang wrote the first prototype for Perl 6, Pugs, in Haskell, and coined the phrase "lambdacamel" for the substantial crossover between the languages.
What does a Perl programmer make of Haskell? What are the lessons that can be learned (in either direction). And do the languages have more in common than you might have thought?
The basics of Python are rather straightforward. In a few minutes you can learn most of the syntax. There are some gotchas along the way that might appear tricky. This talk is meant to bring programmers up to speed with Python. They should be able to read and write Python.
METHODS DESCRIPTION
copy() They copy() method returns a shallow copy of the dictionary.
clear() The clear() method removes all items from the dictionary.
pop() Removes and returns an element from a dictionary having the given key.
popitem() Removes the arbitrary key-value pair from the dictionary and returns it as tuple.
get() It is a conventional method to access a value for a key.
dictionary_name.values() returns a list of all the values available in a given dictionary.
str() Produces a printable string representation of a dictionary.
update() Adds dictionary dict2’s key-values pairs to dict
setdefault() Set dict[key]=default if key is not already in dict
keys() Returns list of dictionary dict’s keys
items() Returns a list of dict’s (key, value) tuple pairs
has_key() Returns true if key in dictionary dict, false otherwise
fromkeys() Create a new dictionary with keys from seq and values set to value.
type() Returns the type of the passed variable.
cmp() Compares elements of both dict.
Following a game show format made popular by Joshua Bloch and Neal Gafter's Java Puzzlers this presentation intends to both entertain and inform. Snippets of Python code the whose behaviour is not entirely obvious are shown, the audience will then be asked to pick from a number of options what the behaviour of the program is. The correct and sometimes non-intuitive answer will then be given along with a brief explanation of the idea the puzzle exposes. Only a modest working knowledge of the Python language is required to understand the puzzles, but the puzzles may also entertain the more experienced Python programmer.
Python 101++: Let's Get Down to Business!Paige Bailey
You've started the Codecademy and Coursera courses; you've thumbed through Zed Shaw's "Learn Python the Hard Way"; and now you're itching to see what Python can help you do. This is the workshop for you!
Here's the breakdown: we're going to be taking you on a whirlwind tour of Python's capabilities. By the end of the workshop, you should be able to easily follow any of the widely available Python courses on the internet, and have a grasp on some of the more complex aspects of the language.
Please don't forget to bring your personal laptop!
Audience: This course is aimed at those who already have some basic programming experience, either in Python or in another high level programming language (such as C/C++, Fortran, Java, Ruby, Perl, or Visual Basic). If you're an absolute beginner -- new to Python, and new to programming in general -- make sure to check out the "Python 101" workshop!
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12. Zen of Python
• Beautiful is better than ugly.
• Explicit is better than implicit.
• Simple is better than complex.
• Complex is better than complicated.
• Flat is better than nested.
• Sparse is better than dense.
• Readability counts.
• Special cases aren't special enough to break the rules.
• Although practicality beats purity.
• Errors should never pass silently.
• Unless explicitly silenced.
• In the face of ambiguity, refuse the temptation to guess.
• There should be one-- and preferably only one --obvious way to do it.
• Although that way may not be obvious at first unless you're Dutch.
• Now is better than never.
• Although never is often better than *right* now.
• If the implementation is hard to explain, it's a bad idea.
• If the implementation is easy to explain, it may be a good idea.
• Namespaces are one honking great idea -- let's do more of those!