The Anaconda team presented a number of talks and tutorials at SciPy 2018, held in our hometown of Austin, Texas on July 9-15. Check out the video recordings below!

Tutorial: Parallelizing Scientific Python with Dask
Dask is a flexible tool for parallelizing Python code on a single machine or across a cluster. It builds upon familiar tools in the SciPy ecosystem (e.g. NumPy and Pandas) while allowing them to scale across multiple cores or machines. This tutorial covered both the high-level use of dask collections, as well as the low-level use of dask graphs and schedulers.
Anaconda Presenters: Jim Crist, Martin Durant
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Tutorial: PyViz: Easy Visualization and Exploration for All Your Data
This tutorial demonstrated how to use the PyViz suite of tools to quickly build simple or complex visualizations that reveal and give insight into your data. The presenters gave users the tools and know-how to effectively explore, analyze, and visualize even large and complex datasets easily, concisely, and reproducibly.
Anaconda Presenters: James Bednar, Jean-Luc Stevens, Christopher Ball
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Tutorial: pandas .head() to .tail()
This tutorial gave an introduction to pandas, a library providing data structures and algorithms for tabular data analysis. It was aimed at scientists and data analysts new to scientific Python.
Anaconda Co-Presenter: Tom Augspurger
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Tutorial: The Sheer Joy of Packaging
This tutorial covered packaging from start to finish for both PyPI and conda, including setup.py, flit, wheels, twine, conda-build, scikit-build, anaconda cloud, and conda-forge. Particular attention was paid to critical details, such as binary compatibility and platform differences.
Anaconda Co-Presenters: Michael Sarahan, Jonathan Helmus, Ray Donnelly
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Talk: Scikit-build: A Build System Generator for CPython C/C++/Fortran/Cython Extensions
Michael and his co-presenters demonstrated “Scikit-build,” an improved build system generator for CPython C/C++/Fortran/Cython extensions.
Anaconda Co-Presenter: Michael Sarahan
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Talk: EarthSim: Flexible Environmental Simulation Workflows Entirely Within Jupyter Notebooks
Building environmental simulation workflows is typically a slow process involving multiple proprietary desktop tools that do not interoperate well. This talk demonstrated building flexible, lightweight workflows entirely in Jupyter notebooks.
Anaconda Co-Presenters: James Bednar, Christopher Ball, Philipp Rudiger
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Talk: Scalable Machine Learning with Dask
This talk demonstrated how to scale a Python-based machine learning workflow to larger models and larger datasets. The presenters introduced a common workflow using NumPy, pandas, and scikit-learn, and discussed some challenges with scaling that workflow out to larger datasets.
Anaconda Co-Presenter: Tom Augspurger
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Talk: PyViz: Unifying Python Tools for In-Browser Data Visualization
This talk introduced PyViz.org, a new initiative to integrate existing OSS tools into a full suite that solves a wide range of problems in data exploration and communication.
Anaconda Presenters: James Bednar, Jean-Luc Stevens, Philipp Rudiger, Christopher Ball, Bryan Van de Ven
Watch video.