Wes McKinney is the main author of pandas, the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities. He worked as aquantitative analyst at AQR Capital Management and Python consultantbefore founding DataPad, a data analytics company, in 2013. Hegraduated from MIT with an S.B. in Mathematics.
The course is a hands-on introduction to the fundamentals of programming, data structures and algorithms for data sciences. The course encompasses programming basics such as functions, data structures, and algorithms; and their use in design and implementation of data science applications. Fundamentals of discrete mathematics for programming with data will be introduced where appropriate. The course will also introduce students to programming as a collaborative discipline. The course will develop programming experience in Python.
Python For Data Analysis: Data Wrangling With Pandas, NumPy, And IPythonl
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
Given that most modern tools and languages can call JSON-oriented REST services either directly or through well-vetted libraries, cyREST enables near-universal access to Cytoscape. However, such tools and languages often define data structures well tuned for use with their own specialized libraries that manipulate network-oriented data. To ease and accelerate the programming process, cyREST provides harmonization libraries designed to make calling cyREST natural and native within a tool or language. Harmonization libraries are described below.
This section describes both the cyREST design and implementation and the implementation of harmonization libraries. It then presents example workflows created by combining standard data analysis tools with Cytoscape/cyREST.
cyREST is a Cytoscape app that exposes the Cytoscape network data model to external tools and languages. It presents an API based on principles of REST, as do other popular biology-related data services, including those provided by EBI 19 . As a result, cyREST leverages REST facilities in existing tools and languages already built and vetted for use with other REST-based services. The definition and packaging of individual API functions takes advantage of lessons learned in building similar interfaces for Cytoscape 2.
cyREST APIs represent all Cytoscape data objects and functions as resources according to principles of Resource-oriented Design (ROD) 20 . Data objects include networks, tables, and Visual Styles. Functions include applying layout algorithms on networks, updating Visual Styles, and performing statistical analysis. Under REST and ROD, each resource is encoded as a URL where hierarchy is represented as segments within the URL. For example, the URL :1234/v1/tables/count can be decomposed into a REST server ( ), port number (1234), an API version (v1), a resource (tables), and an attribute of the resource (count). So, this URL represents the count of global tables maintained by Cytoscape. Table 2 shows a sampling of resources available under the :1234/v1 URL, with a more comprehensive list in the cyREST document at ).
R is a particularly important platform for biologists because of the complimentary Bioconductor library. We are collaborating with the Bioconductor group to produce the RCy3 harmonization library for R 27 , which leverages cyREST to realize native R network visualization, analysis, and publishing functions. Its igraph, graph 28 , and RBGL 29 packages are useful components for network data analysis workflows.
A typical workflow performs data acquisition and integration, analysis, network visualization, and publishing. Often, these steps are performed one at a time by humans executing one discreet tool after another, possibly resulting in high labor costs, low throughput, high error rates, and an inability to reproduce the workflow reliably. In contrast, Figure 1 shows a workflow orchestrated by external tools such as Python and R, which interact with Cytoscape to perform parts of the workflow. As supplementary material, we provide downloadable sample workflows that incorporate and demonstrate cyREST functionality using py2cytoscape and RCy3 harmonization libraries.
Our Python-based sample workflows are simple reflections of real world data analysis and visualization pipelines (see Figure 1) and use standard Python packages as much as possible. They are located in -rest-python and are viewable using the nbviewer web application ( ) in Jupyter Notebook format.
The authors describe a Cytoscape app, CyREST, which exposes core Cytoscape functions as REST APIs for external software components to process network related data sets in automatic and reproducible workflows built using almost any programming languages. Users of workflows can visualize network data in Cytoscape via its powerful visualization features. The accompanied harmonization libraries for Python and R make the use of CyREST much easier and simpler. The manuscript is well organized, and the described app should be highly valuable for users working with big data related to networks for analysis and visualization.
Vectorized computing is introduced in this lesson by working with NumPy arrays. NumPy arrays are a data structure that consists of a single type of data and can be one, two, or many-dimensioned. The lesson shows how to specify single elements and slices of the array and how to carry out several simple types of calculations. Image manipulations are used as a practical example to illustrate these principles.
PythonFashionForecaster is an ongoing open source code project that I'd like to present to the PyData Community in order to initiate discussion about applications of Python in a traditionally non data-centric industry. It will hopefully extend the use of Python and open source to the world of fashion. A quick search of python repositories on github show a lack of true fashion apps, those mostly involving weather forecast or shopping tools rather than specifically fashion styles. On the other spectrum of fashion apps, those highly relevant to fashion styles are commercial. PythonFashionForecaster is different in that the objective is to display fashion style trends as an information resource in an automatic and computational manner.
This talk would be of interest to anyone that would like to see a case study on the application of parsing JSON data with Python, a survey of data analysis libraries that can be use to analyze social data, as well as anyone interested in fashion related topics. I believe that indirectly this project will bring exposure to the Python Open Source community in non-traditional domains.
You can use Bitdeli to create real-time dashboards and reports, or asa quick and robust way to experiment with up to terabytes of real-timedata. Bitdeli is based on vanilla Python to maximize developer-friendliness. There is no need to learn a new paradigm or stop using existing Python packages.
In 1967 sociologist Stanley Milgram began a series of experiments into the "small world problem" that would firmly cement the phrase "six degrees of separation" within the popular culture. Because of these experiments, nearly all of us today have heard that we are simply a few hand shakes away from anyone in the world. Indeed it's a popular past time amongst academics to figure our their Erdos number and, amongst the rest of us, to calculate a favorite actor's Bacon number. Fast forward to today and the world seems even smaller. With the internet connecting all of us to one another at the speed of light, and social networks such as Twitter and Facebook creating communities that quite literally span the globe, this new era in connectedness has given us a wealth of data about how we interact with one another. There's hardly anyone in the tech community today who hasn't heard of social network analysis, but this combination of sociology, computer science, and mathematics has significance beyond just the analysis of social networks.
The goal of this talk is to give the attendees a basic understanding of what network science is and what it can be used for, as well as demonstrate its use in a specific scenario. During the course of this talk we'll walk through a proper definition of a network and introduce some of the jargon necessary to converse with others working in the field. We'll also take a look at some of the statistical properties of networks and how to use them to analyze our own networks. Finally, we'll look at a specific example of the application of network science principles on a real life social network. By the end of the talk, an attendee should feel comfortable enough with field of network science to be able to start analyzing their own networks of data.
The goal of Disco has been to be a simple and usable implementation ofMapReduce. To keep things simple, this MapReduce aspect has beenhard-coded into Disco, both in the Erlang job scheduler, as well as inthe Python library. To fix various issues in the implementation, wedecided to take a cold hard look at the dataflow in Disco's version ofMapReduce. We came up with a generalization that should be moreflexible and hence also more useful than plain old MapReduce. We callthis the Pipeline model, and we hope to use this in the next majorrelease of Disco. This will implement the old MapReduce model interms of a more general programmable pipeline, and also expose thepipeline to users wishing to take advantage of the optimizationopportunities it offers. 2ff7e9595c
コメント