Introduction to Python Classes (Part 1 of 2) | Python Central Classes are a way of grouping related bits of information together into a single unit (also known as an object), along with functions that can be called to manipulate that object (also known as methods). For example, if you want to track information about a person, you might want to record their name, address and phone number, and be able to manipulate all of these as a single unit. Python has a slightly idiosyncratic way of handling classes, so even if you're familiar with object-oriented languages like C++ or Java, it's still worth digging into Python classes since there are a few things that are different. Before we start, it's important to understand the difference between a class and an object. Let's start off by defining a simple class: The class is called Foo and as usual, we use indentation to tell Python where the class definition starts and ends. The diagram shows what the class definition looks like - 2 methods and 1 member variable. Let's create another one:
Symmetry and Groups | World of Mathematics Symmetry is beautiful. Symmetry appears everywhere in nature. And symmetry can be explained using mathematics. There are three basic kinds of symmetry: Think about where these symmetries appear in nature. Some objects can have several symmetries at once, such as both reflectional and rotational symmetry, or several axes of reflectional symmetry. Two shapes which are the same after reflection, rotation or translation (moving) are called congruent. Let us consider the square as a simple example. This collection of symmetries of a square has very special properties. Note that whenever we add or subtract two rotations or two reflections we get a rotation. Symmetry Calculator When adding more than two symmetries, we can use brackets to specify the order. could mean ( , or it could mean ). It does however matter which way round we add two symmetries – they are not commutative. but and . We say that are generators for this collection of symmetries – they can be used to generate all the others.
Python For Beginners Welcome! Are you completely new to programming? If not then we presume you will be looking for information about why and how to get started with Python. Fortunately an experienced programmer in any programming language (whatever it may be) can pick up Python very quickly. It's also easy for beginners to use and learn, so jump in! Installing Python is generally easy, and nowadays many Linux and UNIX distributions include a recent Python. If you want to know whether a particular application, or a library with particular functionality, is available in Python there are a number of possible sources of information. If you have a question, it's a good idea to try the FAQ, which answers the most commonly asked questions about Python. If you want to help to develop Python, take a look at the developer area for further information.
The Real Python Course Bundle – Real Python Real Python teaches programming and web development through hands-on, interesting examples that are useful and fun! Join the thousands of developers who have already benefited from these unique Python courses and download your copy today: You’ll get three Python courses, with over 1,300 pages of content—packed with exercises, sample files, assignments, and bonus videos. Add To Cart » Or, view the other pricing options. How Will You Benefit From This Python Course? Whether you’re new to programming or a professional software developer looking to dive into a new language, this course will teach you all of the practical Python that you need to get started on projects on your own. Real Python emphasizes real-world programming techniques, which are illustrated through interesting, useful examples. Python is open-source freeware, meaning you can download it for free and use it for any purpose. Python was built to be easier to use than other programming languages. Simple, right? Why This Course?
Fp In Scala | Strictness And Laziness Functional programming in Scala The following set of sections represent the exercises contained in the book "Functional Programming in Scala", written by Paul Chiusano and Rúnar Bjarnason and published by Manning. This content library is meant to be used in tandem with the book. We use the same numeration for the exercises for you to follow them. For more information about "Functional Programming in Scala" please visit its official website. Strict and non-strict functions Exercise 5.1: Now let's write a few helper functions to make inspecting streams easier, starting with a function to convert a Stream to a List (which will force its evaluation): Exercise 5.2: Let's continue by writing the function take for returning the first n elements of a Stream. def cons[A](hd: => A, tl: => Stream[A]): Stream[A] = { lazy val head = hd lazy val tail = tl Cons(() => head, () => tail) } def empty[A]: Stream[A] = Empty drop is similar to take, but skips the first n elements of a Stream instead: Exercise 5.3:
Python's range() Function Explained | Python Central Published: Tuesday 20th August 2013 Last Updated: Monday 8th December 2014 What is Python's range() Function? As an experienced Python developer, or even a beginner, you've likely heard of the Python range() function. But what does it do? The range() function works a little bit differently between Python 2.x and 3.x under the hood, however the concept is the same. Python's range() Parameters The range() function has two sets of parameters, as follows: range(stop) stop: Number of integers (whole numbers) to generate, starting from zero. eg. range(3) == [0, 1, 2]. range([start], stop[, step]) start: Starting number of the sequence.stop: Generate numbers up to, but not including this number.step: Difference between each number in the sequence. Note that: All parameters must be integers.All parameters can be positive or negative.range() (and Python in general) is 0-index based, meaning list indexes start at 0, not 1. eg. Python's range() Function Examples Simple Usage Iterating Lists Brilliant!
Oliver Knill Teaching Some lecture notes Videos Applications of calculusCalculus in 20x20 seconds Oliver Knill, Department of Mathematics, Harvard University, One Oxford Street, Cambridge, MA 02138, USA. SciCenter 432 Tel: (617) 495 5549, Email: knill@math.harvard.edu. twitter, vimeo, Linkedin Spring 2014 office hours: Tue/Thu 4-5, and by appointment, especially before and after classes. %%title%% Install Python Posted by Derek Banas on Jun 15, 2016 in Web Design | 0 comments I get asked all of the time how to install Python on Windows and Mac so I decided to make a tutorial that shows how to do it. I also show how to set up the Atom text editor so that the code will run in a console inside of it because that is another common request. This is also going to be needed because I... Python Programming I have received a ton of requests to make a Python programming tutorial in which I teach pretty much everything in one video. Python 2.7 Tutorial Pt 19 Well it has been a long trip, but I’m going to wrap up the Python Tutorial here. Python 2.7 Tutorial Pt 18 Chat Server In this Python Tutorial I show you how to build a simple chat server. Python 2.7 Tutorial Pt 17 In this video tutorial I show you how to create dynamic websites with Python. Python 2.7 Tutorial Pt 16 Here I continue to teach you how to use the Tkinter GUI module built into Python. Python 2.7 Tutorial Pt 15
101 NumPy Exercises for Data Analysis (Python) - Machine Learning Plus The goal of the numpy exercises is to serve as a reference as well as to get you to apply numpy beyond the basics. The questions are of 4 levels of difficulties with L1 being the easiest to L4 being the hardest. If you want a quick refresher on numpy, the numpy basics and the advanced numpy tutorials might be what you are looking for. 1. Import numpy as np and see the version Difficulty Level: L1 Q. Show Solution import numpy as np print(np. You must import numpy as np for the rest of the codes in this exercise to work. To install numpy its recommended to use the installation provided by anaconda. 2. Q. Desired output: #> array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) arr = np.arange(10) arr #> array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) 3. Q. np.full((3, 3), True, dtype=bool) #> array([[ True, True, True], #> [ True, True, True], #> [ True, True, True]], dtype=bool) # Alternate method: np.ones((3,3), dtype=bool) 4. Q. Input: arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])` #> array([1, 3, 5, 7, 9]) 5. Q. 6. Q. 7.
Documents du cours de programmation avancée 23 septembre : Les paradigmes de la programmation Télécharger pour écran (en français). 30 septembre : Fonctions d’ordre supérieur Télécharger en français pour écran, pour impression par 2 ou par 4. Télécharger en anglais pour écran, pour impression par 2 ou par 4. 7 octobre : Abstractions de données Télécharger en français pour écran, pour impression par 2 ou par 4. 14 octobre : Filtrage de motifs Télécharger en français pour écran, pour impression par 2 ou par 4. 21 octobre : Complément sur les listes Télécharger en français pour écran, pour impression par 2 ou par 4. 28 octobre : La notation for Télécharger en français pour écran, pour impression par 2 ou par 4. 4 novembre : Calcul symbolique Télécharger en français pour écran, pour impression par 2 ou par 4. 11 novembre : Fonctions et états Télécharger en français pour écran, pour impression par 2 ou par 4. 18 novembre : Contraintes Télécharger en français pour écran, pour impression par 2 ou par 4. 2 décembre : Lisp