43 items tagged “jupyter”
2018
mendoza-trees-workshop (via) Eventbrite Argentina has an academy program to train new Python/Django developers. I presented a workshop there this morning showing how Django and Jupyter can be used together to iterate on a project. Since the session was primarily about demonstrating Jupyter it was mostly live-coding, but the joy of Jupyter is that at the end of a workshop you can go back and add inline commentary to the notebooks that you used. In putting together the workshop I learned about the django_extensions “/manage.py shell_plus --notebook” command—it’s brilliant! It launches Jupyter in a way that lets you directly import your Django models without having to mess around with DJANGO_SETTINGS_MODULE.
Iodide Notebook: Project Examples (via) Iodide is a very promising looking open source JavaScript notebook project, and these examples do a great job of showing what it can do. It’s not as slick (yet) as Observable but it does run completely independently using just a browser.
Creating Simple Interactive Forms Using Python + Markdown Using ScriptedForms + Jupyter (via) ScriptedForms is a fascinating Jupyter hack that lets you construct dynamic documents defined using markdown that provide form fields and evaluate Python code instantly as you interact with them.
Scientific results today are as often as not found with the help of computers. That’s because the ideas are complex, dynamic, hard to grab ahold of in your mind’s eye. And yet by far the most popular tool we have for communicating these results is the PDF—literally a simulation of a piece of paper. Maybe we can do better.
Observable Beta (via) Observable just released their beta, and it’s quite something. It’s by Mike Bostock (d3), Jeremy Ashkenas (Backbone, CoffeeScript) and Tom MacWright (Mapbox Studio). The easiest way to describe it is Jupyter notebooks for JavaScript supporting reactive programming—so code is evaluated as you type and you can add interactive widgets (like sliders and canvas views) to construct explorable visualizations on the fly.
2017
Interactive Workflows for C++ with Jupyter. Whoa, this really works... not just an interactive C++ REPL in a Jupyter notebook, but inline graph plotting support and interactive widgets as well. Scroll to the bottom of the article for Binder links which let you fire up an interactive C++ REPL in your browser and start interacting with it instantly.
Exploring Line Lengths in Python Packages. Interesting exploration of the impact if the 79 character length limit rule of thumb on various Python packages—and a thoroughly useful guide to histogram plotting in Jupyter, pandas and matplotlib.
Using “import refs” to iteratively import data into Django
I’ve been writing a few scripts to backfill my blog with content I originally posted elsewhere. So far I’ve imported answers I posted on Quora (background), answers I posted on Ask MetaFilter and content I recovered from the Internet Archive.
[... 559 words]A Minimalist Guide to SQLite. Pretty comprehensive actually—covers the sqlite3 command line app, importing CSVs, integrating with Python, Pandas and Jupyter notebooks, visualization and more.
Exploring United States Policing Data Using Python. Outstanding introduction to data analysis with Jupyter and Pandas.
Streaming Dataframes. This is some deep and brilliant magic: Matthew Rocklin’s Streamz Python library provides some elegant abstractions for consuming infinite streams of data and calculating cumulative averages and rolling reductions... and now he’s added an integration with jupyter that lets you embed bokeh graphs and pandas dataframe tables that continue to update in realtime as the stream continues! Check out the animated screenshots, this really is a phenomenal piece of work.
Recovering missing content from the Internet Archive
When I restored my blog last weekend I used the most recent SQL backup of my blog’s database from back in 2010. I thought it had all of my content from before I started my 7 year hiatus, but in watching the 404 logs I started seeing the occasional hit to something that really should have been there but wasn’t. Turns out the SQL backup I was working from was missing some content.
[... 636 words]Facets. New open source visualization and data exploration tool from Google (“Disclaimer: This is not an official Google product”, whatever that means). It’s intended for visualizing machine learning datasets but it’s obviously useful outside of ML as well—any time you need to understand a large dataset this looks like it could be extremely useful. Ships with example jupyter notebooks and an easy mechanism for embedding the Facets interactive UI directly inside a notebook cell.