A Notebook is a Hypothesis

---

An interactive computing performance from deathbeds

---

1. The impacts of physical and computational notebooks on science.

2. The lifecycle of hypothesis testing & interactive computing in Jupyter notebooks.

3. Preserving computational essays for reuse.

---

Takeaways

• Computational essays take parts from literacy and programming to tell data driven stories..

• Best practices for maturing notebooks into computational essays.

• Formal software testing practices to extend the sell by date of a notebook.

• All of the artifacts of this presentation and to explore later.

---

Links to this presentation.

github | nbviewer

Code along with binder.

---

Resources for new notebook users.

• Jupyter/IPython Notebook Quick Start Guide

• Try Jupyterlab

• A gallery of interesting Jupyter Notebooks

Useful packages for advanced notebooks users

• importnb

• doctest

• pytest

• mybinder

• nbviewer

• nbconvert

• jupyterlab

• graphviz for diagrams.

Resources for advanced users

• Integrating IPython/Jupyter with your objects

• Learn deep learning in fast.ai notebooks.

Deathbeds

Deathbeds is open source coding collective experimenting with interactive computing and modern science.

• Jyve

  Jupyterlab if pure javascript.

• importnb

  Use notebooks like real modules.

• poser

  A fluent API for python.

    if __name__ == '__main__':
        !jupyter nbconvert --to markdown readme.ipynb

    if __name__ == '__main__':
        import pytest
        pytest.main([])

============================ test session starts =============================
platform darwin -- Python 3.6.3, pytest-3.5.0, py-1.5.3, pluggy-0.6.0
benchmark: 3.1.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /Users/tonyfast/_what_is_a_hypothesis, inifile:
plugins: cov-2.5.1, benchmark-3.1.1, hypothesis-3.56.5, importnb-0.3.1