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Python Data Analysis Library — pandas: Python Data Analysis Library

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The world's most advanced open source database Quiet log noise with Python and machine learning Continuous integration (CI) jobs can generate massive volumes of data. When a job fails, figuring out what went wrong can be a tedious process that involves investigating logs to discover the root cause—which is often found in a fraction of the total job output. To make it easier to separate the most relevant data from the rest, the Logreduce machine learning model is trained using previous successful job runs to extract anomalies from failed runs' logs. This principle can also be applied to other use cases, for example, extracting anomalies from Journald or other systemwide regular log files. Using machine learning to reduce noise A typical log file contains many nominal events ("baselines") along with a few exceptions that are relevant to the developer. Log events must be converted to numeric values for k-NN regression. Once the model is trained, the k-NN search tells us the distance of each new event from the baseline. Introducing Logreduce Managing baselines Project roadmap

The Python Tutorial — Python 3.6.5 documentation Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms. The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python web site, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation. The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). This tutorial introduces the reader informally to the basic concepts and features of the Python language and system.

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Programming languages: Python developers reveal their favorite tools Python may be the world's fastest-growing programming language in terms of popularity but what are developers doing with it and which tools are they using? The Python Software Foundation has shed light on how developers are using Python across the language's three main areas of use: data science, web development and DevOps. More than 20,000 professional and hobbyist developers across 150 countries were polled by the foundation and IDE software company JetBrains for the Python Developers Survey 2018 report in the fall of last year. For the first time, developers are primarily using Python for data analysis, which has overtaken web development as the main role the language is used for. SEE: Hiring kit: Python developer (Tech Pro Research) "Data analysis has become more popular than web development, growing from 50% in 2017 to 58% in 2018," says the report. "Machine learning also grew by 7 percentage points. What Python is used for? Most popular data science frameworks for Python Sign up today

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Put Your IDE in a Container with Guacamole – Red Hat OpenShift Blog Put Your IDE in a Container Apache Guacamole is an incubating Apache project that enables X window applications to be exposed via HTML5 and accessed via a browser. This article shows how Guacamole can be run inside containers in an OpenShift Container Platform (OCP) cluster to enable Red Hat JBoss Developer Studio, the eclipse-based IDE for the JBoss middleware portfolio, to be accessed via a web browser. You’re probably thinking “Wait a minute… X windows applications in a container?” Yes, this is entirely possible and this post will show you how. Bear in mind that tools from organizations like CODENVY can provide a truly cloud-ready IDE. How does Apache Guacamole work? Apache Guacamole consists of two main components, the Guacamole web application (known as the guacamole-client) and the Guacamole daemon (or guacd). Log in to OpenShift Container Platform This was tested on a cloud-based OpenShift installation as well as a laptop using the Red Hat Container Development Kit. oc get pods Tags

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. He recently taught a high school level outreach program at MIT and a fabrication and digital craft research studio, which was supported by an Education Committee Grant at the Boston Architectural College. 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.

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. 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. arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

Easier Python paths with pathlib A look at the benefits of using pathlib, the "object-oriented way of dealing with paths". Working with files is one of the most common things developers do. After all, you often want to read from files (to read information saved by other users, sessions or programs) or write to files (to record data for other users, sessions or programs). Of course, files are located inside directories. Navigating through directories, finding files in those directories, and even extracting information about directories (and the files within them) might be common, but they're often frustrating to deal with. This isn't necessarily bad; the fact is that Python developers have used this combination of modules, methods and files for quite some time. Indeed, it turns out that for several years already, Python's standard library has come with the pathlib module, which makes it easier to work with directories and files. pathlib Basics import pathlib Now that you've done that, you can create a new Path object.

vue-google-charts Reactive Vue.js wrapper for Google Charts lib Table of contents npm i vue-google-charts Default import Install a component globally (use as plugin): Use locally in a component: Browser The plugin should be auto-installed. Read the Google Charts docs first The GChart component is a wrapper for the original Google Charts, so it's assumed you are familiar with the vanilla Google Charts usage ( With vue-google-charts package you don't need to link script loader and load Google Charts package manually. Another bonus — reactive data binding. Simple usage: return chartData: 'Year' 'Sales' 'Expenses' 'Profit' chartOptions: chart: title: 'Company Performance' subtitle: 'Sales, Expenses, and Profit: 2014-2017' Load additional packages: Using settings prop you can specify any setting available for google charts loader: packages, language, callback, mapsApiKey. See more on available setting There's also version prop, so you can load a specific version, e.g. version="upcoming".

What is Tailwind? - Tailwind CSS A utility-first CSS framework for rapidly building custom user interfaces. Tailwind is different from frameworks like Bootstrap, Foundation, or Bulma in that it's not a UI kit. It doesn't have a default theme, and there are no built-in UI components. On the flip side, it also has no opinion about how your site should look and doesn't impose design decisions that you have to fight to undo. If you're looking for a framework that comes with a menu of predesigned widgets to build your site with, Tailwind might not be the right framework for you. But if you want a huge head start implementing a custom design with its own identity, Tailwind might be just what you're looking for. Utility-first Creating a framework for building custom UIs means you can't provide abstractions at the usual level of buttons, forms, cards, navbars, etc. Here's an example of a responsive contact card component built with Tailwind without writing a single line of CSS: Adam Wathan Developer at NothingWorks Inc.

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.