Reproducible, One Button Workflows with the Jupyter Notebook & Scons | SciPy 2016 | Jessica Hamrick
What is the best way to develop analysis code in the Jupyter notebook, while managing complex dependencies between analyses? In this talk, I will introduce nbflow, which is a project that integrates a Python-based build system (SCons) with the Jupyter notebook, enabling researchers to easily build sophisticated, complex analysis pipelines entirely within notebooks while still maintaining a "one-button workflow" in which all analyses can be executed, in the correct order, from a single command. I will show how nbflow can be applied to existing analyses and how it can be used to construct an analysis pipeline stretching the entire way from data cleaning, to computing statistics, to generating figures, and even to automatically generating LaTeX commands that can be used in publications to format results without the risk of copy-and-paste error. More info on nbflow here: https://github.com/jhamrick/nbflow See the complete SciPy 2016 Conference talk & tutorial playlist here: https://www.youtube.com/playlist?list...

Datashader Revealing the Structure of Genuinely Big Data | SciPy 2016 | James A Bednar

Make Jupyter/IPython Notebook even more magical with cell magic extensions!

Constructing Models to Deal with Missing Data | SciPy 2016 | Deborah Hanus

Andrej Karpathy: From Vibe Coding to Agentic Engineering w/ Stephanie Zhan

Something is jamming GPS over Europe. Here's what we found

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Ronny Chieng Address | Harvard Class Day 2026

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Conan O’Brien Delivers the Commencement Address | Harvard Commencement 2026

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Jakub Czakon: 10 things you should know about Jupyter Notebooks | PyData Warsaw 2017

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Teaching with IPython:Jupyter Notebooks and JupyterHub | SciPy 2015 | Jessica Hamrick

Machine Learning for Time Series Data in Python | SciPy 2016 | Brett Naul

