A graph can be used to visualize related data, or to find the shortest path from one node to another node for example. Central concepts in graph theory are: Node: a block of information in the network.Edge: a connection between two nodes (can have a direction and a weight).Centrality: determining the relative importance of a node.Clustering: partitioning nodes into groups.
It’s like an ORM for graphs, but instead of SQL, you use the graph-traveral language Gremlin to query the database. Bulbs supports pluggable backends, and you can use it to connect to either Neo4j Server or Rexster. Neo4j Server is Neo4j‘s official server. Rexster is TinkerPop‘s server, and it supports any Blueprints-enabled database, including Neo4j, OrientDB, Dex, and OpenRDF. This means your code is portable because you can to plug into different graph database backends without worrying about vendor lock in. Bulbs was developed in the process of building Whybase, a startup that will open for preview later this year. You can use Bulbs from within any Python Web-development framework, including Flask, Pyramid, and Django. Code Example Here’s how you model domain objects: Why Graphs? Understanding imports and PYTHONPATH — Stereoplex.
Something I've heard a few times from developers coming to Python from languages such as PHP is that module importing and the PYTHONPATH is a bit of a mystery.
I remember understanding PYTHONPATH when I learned Python since I'd done a bit of Java at university (and PYTHONPATH is conceptually the same as Java's CLASSPATH), but several flavours of import confused me. This post covers both; first we'll talk about the import statement, and then we'll cover PYTHONPATH. Understanding import and from ... import ... Python has two forms of import statement. They look something like this: Orange – Data Mining Fruitful & Fun. SciPy -
Python plotting — Matplotlib 1.2.0 documentation. Machine learning in Python — scikit-learn 0.13 documentation. "We use scikit-learn to support leading-edge basic research [...]
" "I think it's the most well-designed ML package I've seen so far. " "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]. " "For these tasks, we relied on the excellent scikit-learn package for Python. " "The great benefit of scikit-learn is its fast learning curve [...] " "It allows us to do AWesome stuff we would not otherwise accomplish"
The Python Tutorial. Python is an easy to learn, powerful programming language.
The Python Tutorial — Python v3.3.0 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.
The Glossary is also worth going through. Python Programming Language – Official Website. PyDev. Python (programming language) Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles.
It features a dynamic type system and automatic memory management and has a large and comprehensive standard library. Like other dynamic languages, Python is often used as a scripting language, but is also used in a wide range of non-scripting contexts. Using third-party tools, such as Py2exe, or Pyinstaller, Python code can be packaged into standalone executable programs. Python interpreters are available for many operating systems. CPython, the reference implementation of Python, is free and open source software and has a community-based development model, as do nearly all of its alternative implementations.
Python 3.0 (also called Python 3000 or py3k), a major, backwards-incompatible release, was released on 3 December 2008 after a long period of testing. PyBrain.