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Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and <canvas> visualization. The module is free, well-document and bundled with 50+ examples and 350+ unit tests. Download Installation Pattern is written for Python 2.5+ (no support for Python 3 yet). To install Pattern so that the module is available in all Python scripts, from the command line do: > cd pattern-2.6 > python install If you have pip, you can automatically download and install from the PyPi repository: If none of the above works, you can make Python aware of the module in three ways: Quick overview pattern.web pattern.en The pattern.en module is a natural language processing (NLP) toolkit for English. pattern.vector Case studies Related:  logank1

Time Series analysis tsa — statsmodels 0.7.0 documentation statsmodels.tsa contains model classes and functions that are useful for time series analysis. This currently includes univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It also includes methods to work with autoregressive and moving average lag-polynomials. Estimation is either done by exact or conditional Maximum Likelihood or conditional least-squares, either using Kalman Filter or direct filters. Currently, functions and classes have to be imported from the corresponding module, but the main classes will be made available in the statsmodels.tsa namespace. Some related functions are also available in matplotlib, nitime, and scikits.talkbox. Descriptive Statistics and Tests Estimation

Beautiful Soup Documentation — Beautiful Soup v4.0.0 documentation Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. These instructions illustrate all major features of Beautiful Soup 4, with examples. This document covers Beautiful Soup version 4.12.1. You might be looking for the documentation for Beautiful Soup 3. This documentation has been translated into other languages by Beautiful Soup users: Getting help If you have questions about Beautiful Soup, or run into problems, send mail to the discussion group. When reporting an error in this documentation, please mention which translation you’re reading. Here’s an HTML document I’ll be using as an example throughout this document. Running the “three sisters” document through Beautiful Soup gives us a BeautifulSoup object, which represents the document as a nested data structure: $ apt-get install python3-bs4

Computer Networking : Principles, Protocols and Practice | INL: IP Networking Lab Computer Networking : Principles, Protocols and Practice (aka CNP3) is an ongoing effort to develop an open-source networking textbook that could be used for an in-depth undergraduate or graduate networking courses. The first edition of the textbook used the top-down approach initially proposed by Jim Kurose and Keith Ross for their Computer Networks textbook published by Addison Wesley. CNP3 is distributed under a creative commons license. The second edition takes a different approach. The new features of the second edition are : The second edition of the ebook is now divided in two main parts The first part of the ebook uses a bottom-up approach and focuses on the principles of the computer networks without entering into protocol and practical details. Numerous exercises are also provided as well as interactive quizzes that enable the students to verify their understanding of the different chapters and lab experiments with netkit and other software tools. First edition of the textbook

7 Major Players In Free Online Education By Jennifer Berry Imagine a world where free, college-level education was available to almost everyone. Believe it or not, you're living in that world right now. Online education has been around for decades, but in the past couple of years, interest has spiked for massive open online courses, otherwise known as MOOCs, according to Brian Whitmer, co-founder of Instructure, an education technology company that created the Canvas Network, a platform for open online courses. "Since 2012, MOOCs have caught the attention of the educational world due to their potential to disrupt how education is delivered and open up access to anyone with an Internet connection," Whitmer explains. Related: 7 Degrees You Can Earn While Keeping Your Job If this seems too good to be true, you should know that, like many endeavors, students will largely get out of these classes what they put into them. Read on to learn more about seven of the most popular MOOCs and some of the great free classes they offer. Coursera

Tutorial - Learn Python in 10 minutes NOTE: If you would like some Python development done, my company, Stochastic Technologies, is available for consulting. This tutorial is available as a short ebook. The e-book features extra content from follow-up posts on various Python best practices, all in a convenient, self-contained format. Preliminary fluff So, you want to learn the Python programming language but can't find a concise and yet full-featured tutorial. Properties Python is strongly typed (i.e. types are enforced), dynamically, implicitly typed (i.e. you don't have to declare variables), case sensitive (i.e. var and VAR are two different variables) and object-oriented (i.e. everything is an object). Getting help Help in Python is always available right in the interpreter. >>> help(5)Help on int object:(etc etc) >>> dir(5)['__abs__', '__add__', ...] >>> abs. Syntax Python has no mandatory statement termination characters and blocks are specified by indentation. Data types You can access array ranges using a colon (:).

How To Manage 17 Years Of Bookmarks Advertisement If you’ve used the web for any amount of time, you’ve probably built up a huge collection of bookmarks. It’s easy to fill up your bookmarks bar with your most-visited sites, and before long you’ll have an overflowing list of favorite pages that are impossible to navigate. Now is the time to sit down and make your bookmarks more manageable. Let’s look at a process containing tools and tips to clean up, organize, and manage your bookmarks so they’re no longer a nightmare. Step 1: Remove Dead and Duplicate Bookmarks There’s not much point keeping bookmarks to dead links or two links that go to the same page. A free tool for Windows called AM-DeadLink will help here. We notice you're using an adblocker. I've whitelisted MakeUseOf. Not now. Open the software and select your browser from the dropdown at the top-left. Once it’s done, you’ll see the Status of each bookmark. The error, redirected, file not found and other red fields represent dead links. Step 2: Sync Your Bookmarks