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:
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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!
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.
(IT) Information Technology Certifications | CompTIA IT Certifications Beginning Python Programming — Beginning Python Programming for Aspiring Web Developers Navigation Beginning Python Programming¶ for Aspiring Web Developers¶ Using Python 3 by Jeffrey Elkner (with liberal borrowings from the work of Allen B. Last updated: 8 March 2018 Copyright NoticeContributor ListChapter 1 The way of the programChapter 2 Values, expressions, and statementsChapter 3 Strings, lists, and tuplesChapter 4 Conditionals and loopsChapter 5 FunctionsChapter 6 Dictionaries, sets, files, and modulesChapter 7 Classes and objectsChapter 8 InheritanceChapter 9 Server-side scriptingAppendix A Configuring Ubuntu for Python web developmentAppendix B Making Graphs with matplotlibGNU Free Document License Search Page © Copyright 2017, Jeffrey Elkner.
'anaconda' New Answers Computing Form and Shape: Python Programming with the Rhinoscript Library - an Online Programming Course at Kadenze Carl Lostritto conducts research and teaches in the area of computational design with an emphasis on drawing and media. He is currently Assistant Professor of Architecture at RISD. He previously taught architecture and design at The Boston Architectural College, The Catholic University of America, The University of Maryland, and The Massachusetts Institute of Technology. His teaching has spanned all levels of design curricula including introductory and advanced architectural studios, design seminars, and workshops. Concurrent to teaching, he operates a computational design consultancy, which partners with artists, architects, and designers on projects of various types and scales including web design, print media, graphic design, prototyping, installations and buildings. He studied in a post-professional research program at the Massachusetts Institute of Technology within the Design and Computation Group.
Django documentation Everything you need to know about Django. How the documentation is organized¶ Django has a lot of documentation. A high-level overview of how it’s organized will help you know where to look for certain things: Tutorials take you by the hand through a series of steps to create a Web application. Performance and optimization¶ There are a variety of techniques and tools that can help get your code running more efficiently - faster, and using fewer system resources. Performance and optimization overview Geographic framework¶ GeoDjango intends to be a world-class geographic Web framework.