Add Pydata Paris 2024

pydata-paris-2024/category.json

@@ -0,0 +1,3 @@
+{
+  "title": "PyData Paris 2024"
+}

pydata-paris-2024/videos/alexandre-abraham-dreadful-frailties-in-propensity-score-matching-and-how-to-fix-them.json

@@ -0,0 +1,43 @@
+{
+  "description": "In their seminal paper \"\"Why propensity scores should not be used for matching,\"\" King and Nielsen (2019) highlighted the shortcomings of Propensity Score Matching (PSM). Despite these concerns, PSM remains prevalent in mitigating selection bias across numerous retrospective medical studies each year and continues to be endorsed by health authorities. Guidelines to mitigating these issues have been proposed, but many researchers encounter difficulties in both adhering to these guidelines and in thoroughly documenting the entire process.\n\nIn this presentation, I show the inherent variability in outcomes resulting from the commonly accepted validation condition of Standardized Mean Difference (SMD) below 10%. This variability can significantly impact treatment comparisons, potentially leading to misleading conclusions. To address this issue, I introduce A2A, a novel metric computed on a task specifically designed for the problem at hand. By integrating A2A with SMD, our approach substantially reduces the variability of predicted Average Treatment Effects (ATE) by up to 90% across validated matching techniques.\n\nThese findings collectively enhance the reliability of PSM outcomes and lay the groundwork for a comprehensive automated bias correction procedure. Additionally, to facilitate seamless adoption across programming languages, I have integrated these methods into \"\"popmatch,\"\" a Python package that not only incorporates these techniques but also offers a convenient Python interface for R's MatchIt methods.\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps",
+  "duration": 1626,
+  "language": "eng",
+  "recorded": "2024-09-25",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://pydata.org/paris2024"
+    },
+    {
+      "label": "https://github.com/numfocus/YouTubeVideoTimestamps",
+      "url": "https://github.com/numfocus/YouTubeVideoTimestamps"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "Education",
+    "Julia",
+    "NumFOCUS",
+    "Opensource",
+    "PyData",
+    "Python",
+    "Tutorial",
+    "coding",
+    "how to program",
+    "learn",
+    "learn to code",
+    "python 3",
+    "scientific programming",
+    "software"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/y75GbnHizaQ/sddefault.jpg",
+  "title": "Alexandre Abraham - Dreadful Frailties in Propensity Score Matching and How to Fix Them",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=y75GbnHizaQ"
+    }
+  ]
+}

pydata-paris-2024/videos/andro-sabashvili-adaptive-prediction-intervals-pydata-paris-2024.json

@@ -0,0 +1,43 @@
+{
+  "description": "Adaptive prediction intervals, which represent prediction uncertainty, are crucial for practitioners involved in decision-making. Having an adaptivity feature is challenging yet essential, as an uncertainty measure must reflect the model's confidence for each observation. Attendees will learn about state-of-the-art algorithms for constructing adaptive prediction intervals, which is an active area of research.\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps",
+  "duration": 1952,
+  "language": "eng",
+  "recorded": "2024-09-25",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://pydata.org/paris2024"
+    },
+    {
+      "label": "https://github.com/numfocus/YouTubeVideoTimestamps",
+      "url": "https://github.com/numfocus/YouTubeVideoTimestamps"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "Education",
+    "Julia",
+    "NumFOCUS",
+    "Opensource",
+    "PyData",
+    "Python",
+    "Tutorial",
+    "coding",
+    "how to program",
+    "learn",
+    "learn to code",
+    "python 3",
+    "scientific programming",
+    "software"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/t56zcfj51aU/sddefault.jpg",
+  "title": "Andro Sabashvili - Adaptive Prediction Intervals | PyData Paris 2024",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=t56zcfj51aU"
+    }
+  ]
+}

pydata-paris-2024/videos/baggi-orlandi-foundational-models-for-time-series-forecasting-are-we-there-yet-pydata-paris-2024.json

@@ -0,0 +1,43 @@
+{
+  "description": "Transformers are everywhere: NLP, Computer Vision, sound generation and even protein-folding. Why not in forecasting? After all, what ChatGPT does is predicting the next word. Why this architecture isn't state-of-the-art in the time series domain?\n\nIn this talk, you will understand **how Amazon Chronos and Salesforece's Moirai transformer-based forecasting models work**, the **datasets used to train them** and **how to evaluate them** to see if they are a good fit for your use-case.\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps",
+  "duration": 1778,
+  "language": "eng",
+  "recorded": "2024-09-25",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://pydata.org/paris2024"
+    },
+    {
+      "label": "https://github.com/numfocus/YouTubeVideoTimestamps",
+      "url": "https://github.com/numfocus/YouTubeVideoTimestamps"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "Education",
+    "Julia",
+    "NumFOCUS",
+    "Opensource",
+    "PyData",
+    "Python",
+    "Tutorial",
+    "coding",
+    "how to program",
+    "learn",
+    "learn to code",
+    "python 3",
+    "scientific programming",
+    "software"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/lpQg9yxeVSg/sddefault.jpg",
+  "title": "Baggi & Orlandi-Foundational Models for Time Series Forecasting: are we there yet |PyData Paris 2024",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=lpQg9yxeVSg"
+    }
+  ]
+}

pydata-paris-2024/videos/baumann-marchand-visualization-of-the-sky-in-notebooks-the-ipyaladin-widget-extension.json

@@ -0,0 +1,63 @@
+{
+  "description": "[Aladin](https://aladin.cds.unistra.fr/aladin.gml) allows to visualize images of the sky or planetary surfaces just as an astronomical \"\"openstreetmap\"\" app. The view can be panned and explored interactively. In the [ipyaladin widget](https://github.com/cds-astro/ipyaladin) -- that brings Aladin in the Jupyter Notebook environnement -- these abilities are extended with a python API. The users can send astronomical data in standard formats back and forth the viewer and their Python code. Such data can be images of the sky in different wavelengths, but also tabular data, complex shapes that characterize telescope observation regions, or even special sky features (such as probability region for the provenance of a gravitational event).\n\nWith these already existing features, and current work we are doing with the new development framework `anywidget`, `ipyaladin` is really close to a version 1.0.0. It is already used in its beta version in different experimental science platforms, for example in the ESCAPE [European Science Cluster of Astronomy & Particle Physics](https://projectescape.eu/) project and in the experimental [SKA](https://www.skao.int/en/science-users) (Square Kilometre Array, a telescope for radio astronomy) analysis platform.\n\nIn this presentation, we will share our feedback on the development of a widget thanks to `anywidget` compared to the bare `ipywidget` framework. And we will demonstrate the functionalities of the widget through scientific use cases.\n\nhttps://github.com/ManonMarchand/pydata2024\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps",
+  "duration": 1813,
+  "language": "eng",
+  "recorded": "2024-09-25",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://pydata.org/paris2024"
+    },
+    {
+      "label": "https://github.com/cds-astro/ipyaladin",
+      "url": "https://github.com/cds-astro/ipyaladin"
+    },
+    {
+      "label": "https://github.com/ManonMarchand/pydata2024",
+      "url": "https://github.com/ManonMarchand/pydata2024"
+    },
+    {
+      "label": "https://github.com/numfocus/YouTubeVideoTimestamps",
+      "url": "https://github.com/numfocus/YouTubeVideoTimestamps"
+    },
+    {
+      "label": "https://projectescape.eu/",
+      "url": "https://projectescape.eu/"
+    },
+    {
+      "label": "https://www.skao.int/en/science-users",
+      "url": "https://www.skao.int/en/science-users"
+    },
+    {
+      "label": "https://aladin.cds.unistra.fr/aladin.gml",
+      "url": "https://aladin.cds.unistra.fr/aladin.gml"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "Education",
+    "Julia",
+    "NumFOCUS",
+    "Opensource",
+    "PyData",
+    "Python",
+    "Tutorial",
+    "coding",
+    "how to program",
+    "learn",
+    "learn to code",
+    "python 3",
+    "scientific programming",
+    "software"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/L8TUCZlku00/sddefault.jpg",
+  "title": "Baumann & Marchand - Visualization of the sky in Notebooks: the ipyaladin widget extension",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=L8TUCZlku00"
+    }
+  ]
+}

pydata-paris-2024/videos/bel-letoile-carton-leveraging-llms-to-build-supervised-datasets-suitable-for-smaller-models.json

@@ -0,0 +1,43 @@
+{
+  "description": "For some natural language processing (NLP) tasks, based on your production constraints, a simpler custom model can be a good contender to off-the-shelf large language models (LLMs), as long as you have enough qualitative data to build it. The stumbling block being how to obtain such data? Going over some practical cases, we will see how we can leverage the help of LLMs during this phase of an NLP project. How can it help us select the data to work on, or (pre)annotate it? Which model is suitable for which task? What are common pitfalls and where should you put your efforts and focus?\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps",
+  "duration": 1845,
+  "language": "eng",
+  "recorded": "2024-09-25",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://pydata.org/paris2024"
+    },
+    {
+      "label": "https://github.com/numfocus/YouTubeVideoTimestamps",
+      "url": "https://github.com/numfocus/YouTubeVideoTimestamps"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "Education",
+    "Julia",
+    "NumFOCUS",
+    "Opensource",
+    "PyData",
+    "Python",
+    "Tutorial",
+    "coding",
+    "how to program",
+    "learn",
+    "learn to code",
+    "python 3",
+    "scientific programming",
+    "software"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/82AflyGwGWk/sddefault.jpg",
+  "title": "Bel-Letoile & Carton - Leveraging LLMs to build supervised datasets suitable for smaller models",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=82AflyGwGWk"
+    }
+  ]
+}

pydata-paris-2024/videos/breddels-rotko-solara-pure-python-web-apps-beyond-prototypes-and-dashboards-pydata-paris-2024.json

@@ -0,0 +1,47 @@
+{
+  "description": "Many Python frameworks are suitable for creating basic dashboards or prototypes but struggle with more complex ones. Taking lessons from the JavaScript community, the experts on building UI\u2019s, we created a new framework called Solara. Solara scales to much more complex apps and compute-intensive dashboards. Built on the Jupyter stack, Solara apps and its reusable components run in the Jupyter notebook and on its own production quality server based on Starlette/FastAPI.\n\nSolara has a declarative API that is designed for dynamic and complex UIs yet is easy to write. Reactive variables power our state management, which automatically triggers rerenders. Our component-centric architecture stimulates code reusability, and hot reloading promotes efficient workflows. With our rich set of UI and data-focused components, Solara spans the entire spectrum from rapid prototyping to robust, complex dashboards.\n\nhttps://docs.google.com/presentation/d/12Y4YJlJ_YC2DWiYvIZljySwSMPqE9oFuHF_YQxMBFEg/edit#slide=id.g303f45a6bba_0_0\n\nwww.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps",
+  "duration": 1475,
+  "language": "eng",
+  "recorded": "2024-09-25",
+  "related_urls": [
+    {
+      "label": "Conference Website",
+      "url": "https://pydata.org/paris2024"
+    },
+    {
+      "label": "https://docs.google.com/presentation/d/12Y4YJlJ_YC2DWiYvIZljySwSMPqE9oFuHF_YQxMBFEg/edit#slide=id.g303f45a6bba_0_0",
+      "url": "https://docs.google.com/presentation/d/12Y4YJlJ_YC2DWiYvIZljySwSMPqE9oFuHF_YQxMBFEg/edit#slide=id.g303f45a6bba_0_0"
+    },
+    {
+      "label": "https://github.com/numfocus/YouTubeVideoTimestamps",
+      "url": "https://github.com/numfocus/YouTubeVideoTimestamps"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "Education",
+    "Julia",
+    "NumFOCUS",
+    "Opensource",
+    "PyData",
+    "Python",
+    "Tutorial",
+    "coding",
+    "how to program",
+    "learn",
+    "learn to code",
+    "python 3",
+    "scientific programming",
+    "software"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/c0EaM17J78o/sddefault.jpg",
+  "title": "Breddels & Rotko - Solara: Pure Python web apps beyond prototypes and dashboards | PyData Paris 2024",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=c0EaM17J78o"
+    }
+  ]
+}