In this episode of Open Source Directions we were joined by Jeff Bezanson and Katie Hyatt who talk about the work they have been doing with Julia. Julia is a programming language that was designed from the beginning for high performance. It programs compile to native code for multiple platforms via LLVM. Julia is dynamically typed, feels like a scripting language, and has good support for interactive use. Julia has a rich language of descriptive datatypes, and type declarations can be used to clarify and solidify programs. This language uses multiple dispatch as a paradigm, making it easy to express many object-oriented and functional programming patterns. It provides asynchronous I/O, debugging, logging, profiling, a package manager, and more.

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Episode 45: Julia

In this episode of Open Source Directions we were joined by Aditya Mukhopadhyay who talked about the work he has been doing with RecallGraph. RecallGraph is a versioned-graph data store - it retains all changes that its data (vertices and edges) have gone through to reach their current state. It supports point-in-time graph traversals, letting the user query any past state of the graph just as easily as the present.

Episode 44: RecallGraph

In this episode of Open Source Directions we were joined by Matthew Seal who talked about the work he has been doing with Jupyter and Nteract. Matthew also discussed a particular topic: common Jupyter tools and their adoption for various use cases in the wild.

Episode 43: Jupyter & Nteract

OpenTechResponse is the hub for information sharing and coordination between open source projects responding to an emergency or crisis situation.

Episode 42: Open Tech Response

Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It offers a unique combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and beautiful visualization capabilities of a scientific package.

Episode 41: Spyder

Fortran is a compiled language which means that once written, the source code must be passed through a compiler to produce a machine executable that can be run.

Episode 40: Fortran

Apache Arrow is a cross-language development platform for in-memory data. It supports zero-copy streaming messaging and has support for a number of languages, including C, C++, Python, R, Rust, and many others.

Episode 39: Apache Arrow

Jupyter Book lets you build an online book using a collection of Jupyter Notebooks and Markdown files. Its output is similar to the excellent Bookdown tool, and adds extra functionality for people running a Jupyter stack.

Episode 38: Jupyter Book

Originally a port of the R package, pyjanitor has evolved from a set of convenient data cleaning routines into an experiment with the method chaining paradigm. Data preprocessing usually consists of a series of steps that involve transforming raw data into an understandable/usable format.

Episode 37: PyJanitor

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications. This is our second time visiting Bokeh, in preperation for the v2.0 release!

Episode 36: Bokeh 2.0

Lale is a Python library for semi-automated data science. Lale makes it easy to automatically select algorithms and tune hyperparameters of pipelines that are compatible with scikit-learn, in a type-safe fashion. If you are a data scientist who wants to experiment with automated machine learning, this library is for you! Lale adds value beyond scikit-learn along three dimensions: automation, correctness checks, and interoperability. For automation, Lale provides a consistent high-level interface to existing pipeline search tools including GridSearchCV, SMAC, and Hyperopt. For correctness checks, Lale uses JSON Schema to catch mistakes when there is a mismatch between hyperparameters and their type, or between data and operators. And for interoperability, Lale has a growing library of transformers and estimators from popular libraries such as scikit-learn, XGBoost, PyTorch etc. Lale can be installed just like any other Python package and can be edited with off-the-shelf Python tools such as Jupyter notebooks.

Episode 35: IBM Lale

STUMPY is a powerful and scalable library that efficiently computes something called the matrix profile, which can be used for a variety of time series data mining tasks such as: pattern/motif (approximately repeated subsequences within a longer time series) discovery, anomaly/novelty (discord) discovery, shapelet discovery, semantic segmentation, density estimation, time series chains (temporally ordered set of subsequence patterns), and more!

Episode 34: Stumpy

Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

Episode 28: Matplotlib

stdlib ("standard lib") is a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing applications. The library provides a collection of robust, high performance libraries for mathematics, statistics, data processing, streams, and more and includes many of the utilities you would expect from a standard library.

Episode 33: stdlib

Voilà turns Jupyter notebooks into standalone web applications. Unlike the usual HTML-converted notebooks, each user connecting to the Voilà tornado application gets a dedicated Jupyter kernel which can execute the callbacks to changes in Jupyter interactive widgets. By default, Voilà disallows execute requests from the front-end, preventing execution of arbitrary code. By default, Voilà runs with the strip_source option, which strips out the input cells from the rendered notebook.

Episode 32: Voila

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