Mining of Massive Datasets The book has now been published by Cambridge University Press. The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. Cambridge Press does, however, retain copyright on the work, and we expect that you will obtain their permission and acknowledge our authorship if you republish parts or all of it. --- Jure Leskovec, Anand Rajaraman (@anand_raj), and Jeff Ullman Download Version 2.1 The following is the second edition of the book, which we expect to be published soon. There is a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice, rather than how it was described in the original paper. Version 2.1 adds Section 10.5 on finding overlapping communities in social graphs. Download the Latest Book (511 pages, approximately 3MB) Download chapters of the book: Download Version 1.0 Download the Book as Published (340 pages, approximately 2MB)
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Templating Templating, and in particular web templating is a way to represent data in different forms. These forms often (but not always) intended to be readable, even attractive, to a human audience. Frequently, templating solutions involve a document (the template) and data. Template usually looks much like the final output, with placeholders instead of actual data (or example data in simplified form), bears common style and visual elements. Templating Engines There are many, many different HTML/XML templating packages and modules for Python that provide different feature sets and syntaxes. The number of templating engines is so great because the mechanisms involved are pretty easy to write in Python, at least for a fairly basic template engine; this recipe from the Python Cookbook shows how easy it is. Engines using Value Substitution The simplest form of templating engine is that which merely substitutes values into a template in order to produce the final output. HTML Shorthand Processors
Unstructured data is worth the effort when you've got the right tools It’s dawning on companies that data analysis can yield insights and inform business decisions. As data-driven benefits grow, so do our demands about what more data can tell us and what other types we can mine. During her PhD studies, Alyona Medelyan (@zelandiya) developed Maui, an open source tool that performs as well as professional librarians in identifying main topics in documents. Medelyan now leads the research and development of API-based products at Pingar. Pingar senior software researcher Anna Divoli (@annadivoli) studied sentence extraction for semi-automatic annotation of biological databases. “Big data is important in many diverse areas, such as science, social media, and enterprise,” observes Divoli. How did you get started in big data? Anna Divoli: I began working with big data as it relates to science during my PhD. Alyona Medelyan: Like Anna, I mainly focus on unstructured data and how it can be managed using clever algorithms. What projects are you working on now?
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IntegratingPythonWithOtherLanguages [Hint: The idea is to create pages for the stuff, not just link it.] There a various tools which make it easier to bridge the gap between Python and C/C++: Pyrex - write your extension module on Python Cython -- Cython -- an improved version of Pyrex CXX - PyCXX - helper lib for writing Python extensions in C++ SCXX ctypes is a Python module allowing to create and manipulate C data types in Python. These can then be passed to C-functions loaded from dynamic link libraries. elmer - compile and run python code from C, as if it was written in C PicklingTools is a collection of libraries for exchanging Python Dictionaries between C++ and Python. weave - include C code lines in Python program ackward exposes parts of Python's standard library as idiomatic C++ C/C++ Binding Generators Tools to make C/C++ functions/methods accessible from Python by generating binding (Python extension or module) from header files. Articles Related See also to name a few.
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The Python Tutorial 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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Hands-On Python A Tutorial Introduction for Beginners Hands-On Python A Tutorial Introduction for Beginners Contents Chapter 1 Beginning With Python 1.1. You have probably used computers to do all sorts of useful and interesting things. 1.1.1. First let us place Python programming in the context of the computer hardware. z = x+y is an instruction in many high-level languages that means something like: Access the value stored at a location labeled x Calculate the sum of this value and the value stored at a location labeled y Store the result in a location labeled z. No computer understands the high-level instruction directly; it is not in machine language. Obviously high-level languages were a great advance in clarity! If you follow a broad introduction to computing, you will learn more about the layers that connect low-level digital computer circuits to high-level languages. 1.1.2. There are many high-level languages. 1.1.3. If you are not sure whether your computer already has Python, continue to Section 1.2.2 , and give it a try. Windows Linux 1.2.