Brendan O'Brien - Using Qri (“query”) to fetch, query, combine and publish datasets.|JupyterCon 2020

JupyterCon · Intermediate ·🎨 Image & Video AI ·5y ago
Brief Summary Qri is an open-source tool for sharing version-controlled datasets, built on a decentralized network. Using qri-python we can use qri directly from a jupyter notebook, accessing countless community-published datasets for free exploration. In this talk we'll walk through loading version-controlled datasets into a dataframe, running an SQL join, & finally cleaning & publish a dataset of our own. Outline Objective: To show attendees the power of applying principles of open-source to common data resources. Outline: In this talk we’ll first show examples of pulling community-created datasets into qri. We’ll start by browsing http://qri.cloud, pull down a relevant dataset to show single-command access to all qri data without needing to leave Jupyter. We’ll point users to the issue queue for feedback & questions, where they can build an understanding of a dataset. From there we’ll demonstrate publishing a dataset that others can use. We’ll walk through an example that executes an SQL query to joins two qri datasets, perform additional cleanup & annotation, publish a version, and view it on qri cloud. Finally, we’ll talk about some of the challenges of creating a robust data commons, and how qri uses decentralization to solve difficult problems of cost, data availability, and synchronization. By the end we hope attendees will buy into a vision of a world where open data has the same support and tooling as open-source software. Relevant source code: https://github.com/qri-io Video: https://www.youtube.com/watch?v=P2qeY2nPK3Q ------ JupyterCon brings together data scientists, business analysts, researchers, educators, developers, core Project contributors, and tool creators for in-depth training, insightful keynotes, networking, and practical talks exploring the Project Jupyter ecosystem. https://jupytercon.com/ JupyterCon is possible thanks to the generous support of our sponsors, and the labor of many volunteer organizers. https://jupytercon.com/
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Uploads from JupyterCon · JupyterCon · 13 of 60

1 Interview   Joshua Patterson NVIDIA
Interview Joshua Patterson NVIDIA
JupyterCon
2 Dave Stuart - Jupyter as an Enterprise “Do It Yourself” (DIY) Analytic Platform | JupyterCon 2020
Dave Stuart - Jupyter as an Enterprise “Do It Yourself” (DIY) Analytic Platform | JupyterCon 2020
JupyterCon
3 Jeffrey Mew - Supercharge your Data Science workflow | JupyterCon 2020
Jeffrey Mew - Supercharge your Data Science workflow | JupyterCon 2020
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4 Michelle Ufford- Supercharging SQL Users with Jupyter Notebooks | JupyterCon 2020
Michelle Ufford- Supercharging SQL Users with Jupyter Notebooks | JupyterCon 2020
JupyterCon
5 Alan Yu - What we learned from introducing Jupyter Notebooks to the SQL community  | JupyterCon 2020
Alan Yu - What we learned from introducing Jupyter Notebooks to the SQL community | JupyterCon 2020
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6 Chris Holdgraf- 2i2c: sustaining open source through hosted Jupyter infrastructure | JupyterCon 2020
Chris Holdgraf- 2i2c: sustaining open source through hosted Jupyter infrastructure | JupyterCon 2020
JupyterCon
7 Yiwen Li - Intro to Elyra - an AI centric extension for JupyterLab | JupyterCon 2020
Yiwen Li - Intro to Elyra - an AI centric extension for JupyterLab | JupyterCon 2020
JupyterCon
8 Luciano Resende - What's new on Elyra - A set of AI centric JupyterLab extensions | JupyterCon 2020
Luciano Resende - What's new on Elyra - A set of AI centric JupyterLab extensions | JupyterCon 2020
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9 Alan Chin - Explore and Extend AI Pipeline Runtimes with Elyra and JupyterLab | JupyterCon 2020
Alan Chin - Explore and Extend AI Pipeline Runtimes with Elyra and JupyterLab | JupyterCon 2020
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10 Eduardo Blancas- Streamline your Data Science projects with Ploomber | JupyterCon 2020
Eduardo Blancas- Streamline your Data Science projects with Ploomber | JupyterCon 2020
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11 Thorin Tabor - Democratizing the accessibility of computational workflows | JupyterCon 2020
Thorin Tabor - Democratizing the accessibility of computational workflows | JupyterCon 2020
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12 Simon Willison- Using Datasette with Jupyter to publish your data | JupyterCon 2020
Simon Willison- Using Datasette with Jupyter to publish your data | JupyterCon 2020
JupyterCon
Brendan O'Brien - Using Qri (“query”) to fetch, query, combine and publish datasets.|JupyterCon 2020
Brendan O'Brien - Using Qri (“query”) to fetch, query, combine and publish datasets.|JupyterCon 2020
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14 Georgiana Dolocan - Putting the JupyterHub puzzle pieces together | JupyterCon 2020
Georgiana Dolocan - Putting the JupyterHub puzzle pieces together | JupyterCon 2020
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15 Yuvi Panda- Running nonjupyter applications on JupyterHub with jupyter-server-proxy| JupyterCon 2020
Yuvi Panda- Running nonjupyter applications on JupyterHub with jupyter-server-proxy| JupyterCon 2020
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16 Richard Wagner- The Streetwise Guide to JupyterHub Security | JupyterCon 2020
Richard Wagner- The Streetwise Guide to JupyterHub Security | JupyterCon 2020
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17 TamNguyen- Handling Custom Jupyter Data Sources | JupyterCon 2020
TamNguyen- Handling Custom Jupyter Data Sources | JupyterCon 2020
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18 Immanuel Bayer- ipyannotator - the infinitely hackable annotation framework  | JupyterCon 2020
Immanuel Bayer- ipyannotator - the infinitely hackable annotation framework | JupyterCon 2020
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19 Rebecca Kelly- A shared Python, R and Q  Jupyter Notebook - A Quant Sandbox Dream |JupyterCon 2020
Rebecca Kelly- A shared Python, R and Q Jupyter Notebook - A Quant Sandbox Dream |JupyterCon 2020
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20 Itay Dafna - Leap of faith: Transitioning from Excel to Jupyter-based applications | JupyterCon 2020
Itay Dafna - Leap of faith: Transitioning from Excel to Jupyter-based applications | JupyterCon 2020
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21 Damián Avila - Using the Jupyterverse to power MADS | JupyterCon 2020
Damián Avila - Using the Jupyterverse to power MADS | JupyterCon 2020
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22 Chiin Rui Tan- From Zero to Hero | JupyterCon 2020
Chiin Rui Tan- From Zero to Hero | JupyterCon 2020
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23 Firas Moosvi- Teaching an Active Learning class with Jupyter Book| JupyterCon 2020
Firas Moosvi- Teaching an Active Learning class with Jupyter Book| JupyterCon 2020
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24 Daniel Mietchen- Jupyter in the Wikimedia ecosystem | JupyterCon 2020
Daniel Mietchen- Jupyter in the Wikimedia ecosystem | JupyterCon 2020
JupyterCon
25 Qiusheng Wu- How Jupyter and geemap enable interactive mapping and analysis | JupyterCon 2020
Qiusheng Wu- How Jupyter and geemap enable interactive mapping and analysis | JupyterCon 2020
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26 Stephanie Juneau- Jupyterenabled astrophysical analysis for researchers and students|JupyterCon 2020
Stephanie Juneau- Jupyterenabled astrophysical analysis for researchers and students|JupyterCon 2020
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27 Denton Gentry- The Care and Feeding of JupyterHub for Climate Solution Models| JupyterCon 2020
Denton Gentry- The Care and Feeding of JupyterHub for Climate Solution Models| JupyterCon 2020
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28 Tingkai Liu- FlyBrainLab: Interactive Computing in the Connectomic/Synaptomic Era  | JupyterCon 2020
Tingkai Liu- FlyBrainLab: Interactive Computing in the Connectomic/Synaptomic Era | JupyterCon 2020
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29 Kunal Bhalla- A Notebook Style Guide| JupyterCon 2020
Kunal Bhalla- A Notebook Style Guide| JupyterCon 2020
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30 Julia Wagemann - How to avoid 'Death by Jupyter Notebooks' | JupyterCon 2020
Julia Wagemann - How to avoid 'Death by Jupyter Notebooks' | JupyterCon 2020
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31 David Pugh - Best practices for managing Jupyter-based data science  | JupyterCon 2020
David Pugh - Best practices for managing Jupyter-based data science | JupyterCon 2020
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32 Karla Spuldaro - Debugging notebooks and python scripts in JupyterLab | JupyterCon 2020
Karla Spuldaro - Debugging notebooks and python scripts in JupyterLab | JupyterCon 2020
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33 Shreyas Dalia - assert browserTest == True # Frontend Testing JupyterLab  | JupyterCon 2020
Shreyas Dalia - assert browserTest == True # Frontend Testing JupyterLab | JupyterCon 2020
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34 Chris Holdgraf - The new Jupyter Book stack | JupyterCon 2020
Chris Holdgraf - The new Jupyter Book stack | JupyterCon 2020
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35 Hamel Husain - Fastpages - A new, open source Jupyter notebook blogging system | JupyterCon 2020
Hamel Husain - Fastpages - A new, open source Jupyter notebook blogging system | JupyterCon 2020
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36 Marc Wouts - Jupytext: Jupyter Notebooks as Markdown Documents | JupyterCon 2020
Marc Wouts - Jupytext: Jupyter Notebooks as Markdown Documents | JupyterCon 2020
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37 Sheeba Samuel- ProvBook |JupyterCon 2020
Sheeba Samuel- ProvBook |JupyterCon 2020
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38 Philipp Rudiger - To Jupyter and back again | JupyterCon 2020
Philipp Rudiger - To Jupyter and back again | JupyterCon 2020
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39 Jacob Tomlinson - What is my GPU doing? | JupyterCon 2020
Jacob Tomlinson - What is my GPU doing? | JupyterCon 2020
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40 Afshin Darian - A visual debugger in Jupyter | JupyterCon 2020
Afshin Darian - A visual debugger in Jupyter | JupyterCon 2020
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41 Eric Charles - Jupyter Real Time Collaboration| JupyterCon 2020
Eric Charles - Jupyter Real Time Collaboration| JupyterCon 2020
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42 Devin Robison - Optimizing model performance | JupyterCon 2020
Devin Robison - Optimizing model performance | JupyterCon 2020
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43 Junhua zhao - PayPal Notebooks: ML & Data Science experience | JupyterCon 2020
Junhua zhao - PayPal Notebooks: ML & Data Science experience | JupyterCon 2020
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44 April Wang - Redesigning Notebooks for Better Collaboration | JupyterCon 2020
April Wang - Redesigning Notebooks for Better Collaboration | JupyterCon 2020
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45 Bryan Weber - Distributing and Collecting Jupyter Notebooks for Manual Grading| JupyterCon 2020
Bryan Weber - Distributing and Collecting Jupyter Notebooks for Manual Grading| JupyterCon 2020
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46 Georgiana Dolocan - The Littlest JupyterHub distribution | JupyterCon 2020
Georgiana Dolocan - The Littlest JupyterHub distribution | JupyterCon 2020
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47 Tim Metzler - Electronic Examination using Jupyter Notebook | JupyterCon 2020
Tim Metzler - Electronic Examination using Jupyter Notebook | JupyterCon 2020
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48 Blaine Mooers - Why develop a snippet library for Jupyter in your subject domain? | JupyterCon 2020
Blaine Mooers - Why develop a snippet library for Jupyter in your subject domain? | JupyterCon 2020
JupyterCon
49 Ryan Abernathey - Cloud Native Repositories for Big Scientific Data | JupyterCon 2020
Ryan Abernathey - Cloud Native Repositories for Big Scientific Data | JupyterCon 2020
JupyterCon
50 Tanya Rai - Introducing Bento: Jupyter Notebooks @ Facebook | JupyterCon 2020
Tanya Rai - Introducing Bento: Jupyter Notebooks @ Facebook | JupyterCon 2020
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51 Kenton McHenry - From Papers to Notebooks | JupyterCon 2020
Kenton McHenry - From Papers to Notebooks | JupyterCon 2020
JupyterCon
52 Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
JupyterCon
53 Ana Ruvalcaba - Community building is a sustainability strategy | JupyterCon 2020
Ana Ruvalcaba - Community building is a sustainability strategy | JupyterCon 2020
JupyterCon
54 Martin Renou - Xeus: an ecosystem of Jupyter kernels | JupyterCon 2020
Martin Renou - Xeus: an ecosystem of Jupyter kernels | JupyterCon 2020
JupyterCon
55 Michael Wilson - Teaching teenagers to understand Dark Energy | JupyterCon 2020
Michael Wilson - Teaching teenagers to understand Dark Energy | JupyterCon 2020
JupyterCon
56 Davide De Marchi - Voilà dashboards for policy support | JupyterCon 2020
Davide De Marchi - Voilà dashboards for policy support | JupyterCon 2020
JupyterCon
57 Marcos Lopez Caniego - ESASky's JupyterLab widget| JupyterCon 2020
Marcos Lopez Caniego - ESASky's JupyterLab widget| JupyterCon 2020
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58 Praveen Kanamarlapud - Kernel Life Cycle Management | JupyterCon 2020
Praveen Kanamarlapud - Kernel Life Cycle Management | JupyterCon 2020
JupyterCon
59 Aaron Bray - Pulse Physiology Engine | JupyterCon 2020
Aaron Bray - Pulse Physiology Engine | JupyterCon 2020
JupyterCon
60 Aaron Watters - Using WebGL2 transform/feedback in Jupyter widgets | JupyterCon 2020
Aaron Watters - Using WebGL2 transform/feedback in Jupyter widgets | JupyterCon 2020
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