The document discusses Ruby's % notation for string interpolation and formatting. It shows examples of %, %Q, %q, %w, %W, %r, %s, %x, and % notation and what they return. It also discusses substring matching, blocks and procs, tap, and multiple assignment in Ruby.
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.
PLOTCON NYC: Behind Every Great Plot There's a Great Deal of WranglingPlotly
If you are struggling to make a plot, tear yourself away from stackoverflow for a moment and ... take a hard look at your data. Is it really in the most favorable form for the task at hand? Time and time again I have found that my visualization struggles are really a symptom of unfinished data wrangling. R has long had excellent facilities for data aggregation or "split-apply-combine": split an object into pieces, compute on each piece, and glue the result back together again. Recent developments, especially in the purrr package, have made "split-apply-combine" even easier and more general. But this requires a certain comfort level with lists, especially with lists that are columns inside a data frame. This is unfamiliar to most of us. I give an overview of this set of problems and match them up with solutions based on grouped, nested, and split data frames.
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.
PLOTCON NYC: Behind Every Great Plot There's a Great Deal of WranglingPlotly
If you are struggling to make a plot, tear yourself away from stackoverflow for a moment and ... take a hard look at your data. Is it really in the most favorable form for the task at hand? Time and time again I have found that my visualization struggles are really a symptom of unfinished data wrangling. R has long had excellent facilities for data aggregation or "split-apply-combine": split an object into pieces, compute on each piece, and glue the result back together again. Recent developments, especially in the purrr package, have made "split-apply-combine" even easier and more general. But this requires a certain comfort level with lists, especially with lists that are columns inside a data frame. This is unfamiliar to most of us. I give an overview of this set of problems and match them up with solutions based on grouped, nested, and split data frames.
Simplifying code monster to elegant in n 5 stepstutec
In this workshop we'll learn how to transform complex, highly coupled code into a simpler, more readable and maintainable shape. We'll target known software anomalies with Refactoring Patterns, following steps with a confined scope, assuring that we stay distant from "changed everything" commits while achieving quick design improvements.
We'll talk different solutions for Fat Models, God Objects, long method chains, NoMethodError on nils, long methods, bad naming and cold coffee.
Slides presented in RailsConf 2014.
1024+ Seconds of JS Wizardry - JSConf.eu 2013Martin Kleppe
We spend our days creating large-scale applications byte by byte. But what happens at night when we get rid of bloated libraries and browser dependencies? What will we discover deep under the surface if we dissect the language of the web into its atomic parts?
In this talk we will hack tweet-sized games, write code in only six characters and create the self-modifying “Hello World” in less than 1024 bytes of JavaScript. All just for fun and without asking “Why?”.
Prepare yourself for 140 slides full of old-school ASCII art and crazy code golfing!
More info here: http://2013.jsconf.eu/speakers/martin-kleppe-1024-seconds-of-js-wizardry.html
در این جلسه از کلاس به ساختار های داده
Set, Tuple, Dictionary
پرداختیم
PySec101 Fall 2013 J3E1 By Mohammad Reza Kamalifard
Talk About :
Sets,Tuples and Dictionary Data Types in Python
Simplifying code monster to elegant in n 5 stepstutec
In this workshop we'll learn how to transform complex, highly coupled code into a simpler, more readable and maintainable shape. We'll target known software anomalies with Refactoring Patterns, following steps with a confined scope, assuring that we stay distant from "changed everything" commits while achieving quick design improvements.
We'll talk different solutions for Fat Models, God Objects, long method chains, NoMethodError on nils, long methods, bad naming and cold coffee.
Slides presented in RailsConf 2014.
1024+ Seconds of JS Wizardry - JSConf.eu 2013Martin Kleppe
We spend our days creating large-scale applications byte by byte. But what happens at night when we get rid of bloated libraries and browser dependencies? What will we discover deep under the surface if we dissect the language of the web into its atomic parts?
In this talk we will hack tweet-sized games, write code in only six characters and create the self-modifying “Hello World” in less than 1024 bytes of JavaScript. All just for fun and without asking “Why?”.
Prepare yourself for 140 slides full of old-school ASCII art and crazy code golfing!
More info here: http://2013.jsconf.eu/speakers/martin-kleppe-1024-seconds-of-js-wizardry.html
در این جلسه از کلاس به ساختار های داده
Set, Tuple, Dictionary
پرداختیم
PySec101 Fall 2013 J3E1 By Mohammad Reza Kamalifard
Talk About :
Sets,Tuples and Dictionary Data Types in Python
Search Engines in the fight against Institutional ImpecuniousnessIWMW
Plenary talk given by David Hawking at The Institutional Web Management Workshop 2011, held at the University of Reading from Tuesday 26th to Wednesday 27th July 2011.
A slightly-modified version of my IPRUG talk, this time for the BT DevCon5 developer conference at Adastral Park on 25 May 2012.
The main changes are the addition of the Ruby section and the increased number of HHGTTG references in honour of towel day.
Are web managers still needed when everyone is a web 'expert'?IWMW
Slides for talk given by Susan Farrell at the IWMW 2010 event organised by UKOLN and held at the University of Sheffield on 12-14 July 2010.
For further information see
http://iwmw.ukoln.ac.uk/iwmw2010/talks/farrell/
As a result of an engine rewrite with focus on more efficient data structures, PHP 7 offers much improved performance and memory usage. This session describes important aspects of the new implementation and how it compares to PHP 5. A particular focus will be on the representation of values, arrays and objects.
A few techniques for everyday Ruby hacking
Touching on the following topics:
DRY Assignment
Ternary operator
Bang bang
Conditional assignment
Parallel assignment
Multiple return
Implied begin
Exception lists
Symbol to Proc
MapReduce
Regex captures
tap
sprintf
case equality
Splat Array
Splat args
blank?
present?
presence
truncate
try
in?
Delegation
delegate
Memoization
memoize
alias_method_chain
class_attribute
HashWithIndifferentAccess
RedDot Ruby Conf 2014 - Dark side of ruby Gautam Rege
I love Ruby! But as in any relationship, to love means that you (often) have to accept the “dark side” too! Ruby is human in nature and has a lot of gotchas, tricks, weirdness and sometimes scary features that I plan to highlight. This talk aims to provide the “Ah-ha!” moments when working in Ruby.
This talk is for beginners and experts alike - in fact, I tag slides to mark their level and beginners can choose to tune out of the heavy stuff! My talk shall cover the dark side of the following features of Ruby (in no particular order)
Keyword wierdness
method missing
Module inheritance! (huh?)
Accessor righteousness
Curried Procs for the hungry
Base Conversions
Cherry picking module methods
Oniguruma games
Object id weirdness
procs, blocks and our friend stabby.
==, ===, eql? and equal?
and more…
As with most of my talks, humor plays an important role and I shall aim to get everyone high on Ruby with a deep dive!
Yet another rehash of the same old TDD/BDD presentation.
This one was for the BT Adastral Park Software Craftsmanship Mini-Conference on 8 Sepctember 2009.
247. Constructors
class Colour
def self.new_with_hex(*args, &block)
instance = allocate
instance.init_with_hex(*args, &block)
instance
end
248. Constructors
class Colour
def self.new_with_hex(*args, &block)
instance = allocate
instance.init_with_hex(*args, &block)
instance
end
def init_with_hex(name)
...
end
end