diff --git a/pydata-paris-2024/category.json b/pydata-paris-2024/category.json new file mode 100644 index 000000000..70d0c9855 --- /dev/null +++ b/pydata-paris-2024/category.json @@ -0,0 +1,3 @@ +{ + "title": "PyData Paris 2024" +} diff --git a/pydata-paris-2024/videos/alexandre-abraham-dreadful-frailties-in-propensity-score-matching-and-how-to-fix-them.json b/pydata-paris-2024/videos/alexandre-abraham-dreadful-frailties-in-propensity-score-matching-and-how-to-fix-them.json new file mode 100644 index 000000000..a046e85a1 --- /dev/null +++ b/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" + } + ] +} diff --git a/pydata-paris-2024/videos/andro-sabashvili-adaptive-prediction-intervals-pydata-paris-2024.json b/pydata-paris-2024/videos/andro-sabashvili-adaptive-prediction-intervals-pydata-paris-2024.json new file mode 100644 index 000000000..be22c1672 --- /dev/null +++ b/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" + } + ] +} diff --git a/pydata-paris-2024/videos/baggi-orlandi-foundational-models-for-time-series-forecasting-are-we-there-yet-pydata-paris-2024.json b/pydata-paris-2024/videos/baggi-orlandi-foundational-models-for-time-series-forecasting-are-we-there-yet-pydata-paris-2024.json new file mode 100644 index 000000000..16c710487 --- /dev/null +++ b/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" + } + ] +} diff --git a/pydata-paris-2024/videos/baumann-marchand-visualization-of-the-sky-in-notebooks-the-ipyaladin-widget-extension.json b/pydata-paris-2024/videos/baumann-marchand-visualization-of-the-sky-in-notebooks-the-ipyaladin-widget-extension.json new file mode 100644 index 000000000..fb81d17da --- /dev/null +++ b/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" + } + ] +} diff --git a/pydata-paris-2024/videos/bel-letoile-carton-leveraging-llms-to-build-supervised-datasets-suitable-for-smaller-models.json b/pydata-paris-2024/videos/bel-letoile-carton-leveraging-llms-to-build-supervised-datasets-suitable-for-smaller-models.json new file mode 100644 index 000000000..79eb4a49c --- /dev/null +++ b/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" + } + ] +} diff --git a/pydata-paris-2024/videos/breddels-rotko-solara-pure-python-web-apps-beyond-prototypes-and-dashboards-pydata-paris-2024.json b/pydata-paris-2024/videos/breddels-rotko-solara-pure-python-web-apps-beyond-prototypes-and-dashboards-pydata-paris-2024.json new file mode 100644 index 000000000..c6be2be4d --- /dev/null +++ b/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" + } + ] +} diff --git a/pydata-paris-2024/videos/carton-tiran-cappello-mlops-at-renault-a-generic-pipeline-for-scalable-deployment.json b/pydata-paris-2024/videos/carton-tiran-cappello-mlops-at-renault-a-generic-pipeline-for-scalable-deployment.json new file mode 100644 index 000000000..f9818e73e --- /dev/null +++ b/pydata-paris-2024/videos/carton-tiran-cappello-mlops-at-renault-a-generic-pipeline-for-scalable-deployment.json @@ -0,0 +1,43 @@ +{ + "description": "Scaling machine learning at large organizations like Renault Group presents unique challenges, in terms of scales, legal requirements, and diversity of use cases. Data scientists require streamlined workflows and automated processes to efficiently deploy models into production. We present an MLOps pipeline based on python Kubeflow and GCP Vertex AI API designed specifically for this purpose. It enables data scientists to focus on code development for pre-processing, training, evaluation, and prediction. This MLOPS pipeline is a cornerstone of the AI@Scale program, which aims to roll out AI across the Group.\n\nWe choose a Python-first approach, allowing Data scientists to focus purely on writing preprocessing or ML oriented Python code, also allowing data retrieval through SQL queries. The pipeline addresses key questions such as prediction type (batch or API), model versioning, resource allocation, drift monitoring, and alert generation. It favors faster time to market with automated deployment and infrastructure management. Although we encountered pitfalls and design difficulties, that we will discuss during the presentation, this pipeline integrates with a CI/CD process, ensuring efficient and automated model deployment and serving.\n\nFinally, this MLOps solution empowers Renault data scientists to seamlessly translate innovative models into production, and smoothen the development of scalable, and impactful AI-driven solutions.\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": 1925, + "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/CBK6E-NPdWg/sddefault.jpg", + "title": "Carton & Tiran-Cappello - MLOps at Renault: A Generic Pipeline for Scalable Deployment", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=CBK6E-NPdWg" + } + ] +} diff --git a/pydata-paris-2024/videos/cheuk-ting-ho-counting-down-for-cra-updates-and-expectations-pydata-paris-2024.json b/pydata-paris-2024/videos/cheuk-ting-ho-counting-down-for-cra-updates-and-expectations-pydata-paris-2024.json new file mode 100644 index 000000000..7404dd8d6 --- /dev/null +++ b/pydata-paris-2024/videos/cheuk-ting-ho-counting-down-for-cra-updates-and-expectations-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "The EU Commission is likely to vote on the Cyber Resilience Act (CRA) later this year. In this talk we will look at the timeline for the new legislation, any critical discussions happening around implementation and most importantly, the new responsibilities outlined by the CRA. We\u2019ll also discuss what the PSF is doing for CPython and for PyPI and what each of us in the Python ecosystem might want to do to get ready for a new era of increased certainty \u2013 and liability \u2013 around security.\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": 1764, + "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/KkUo8bD-0cE/sddefault.jpg", + "title": "Cheuk Ting Ho - Counting down for CRA: updates and expectations | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=KkUo8bD-0cE" + } + ] +} diff --git a/pydata-paris-2024/videos/christophe-dervieux-exploring-quarto-dashboard-for-impactful-and-visual-communication.json b/pydata-paris-2024/videos/christophe-dervieux-exploring-quarto-dashboard-for-impactful-and-visual-communication.json new file mode 100644 index 000000000..64de755fb --- /dev/null +++ b/pydata-paris-2024/videos/christophe-dervieux-exploring-quarto-dashboard-for-impactful-and-visual-communication.json @@ -0,0 +1,47 @@ +{ + "description": "Embark on a journey to explore how Quarto Dashboard can enhance the narrative of your analysis from your Jupyter Notebook. This talk will show how to create cool interactive charts and graphs that bring your data to life, by using Quarto - an open-source scientific and technical publishing system. \n\nLearn how to make your data communications more engaging and dynamic using Quarto Dashboard. Practical examples and simple explanations will guide you through the process, making it easy to understand and apply to your projects.\n\nhttps://cderv.github.io/pydata-paris-2024-quarto-dashboard/#/title-slide\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": 1781, + "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" + }, + { + "label": "https://cderv.github.io/pydata-paris-2024-quarto-dashboard/#/title-slide", + "url": "https://cderv.github.io/pydata-paris-2024-quarto-dashboard/#/title-slide" + } + ], + "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/7Bw-Dg_xAos/sddefault.jpg", + "title": "Christophe Dervieux - Exploring Quarto Dashboard for impactful and visual communication", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=7Bw-Dg_xAos" + } + ] +} diff --git a/pydata-paris-2024/videos/comte-et-al-onyxia-a-user-centric-interface-for-data-scientists-in-the-cloud-age.json b/pydata-paris-2024/videos/comte-et-al-onyxia-a-user-centric-interface-for-data-scientists-in-the-cloud-age.json new file mode 100644 index 000000000..50ddde2f3 --- /dev/null +++ b/pydata-paris-2024/videos/comte-et-al-onyxia-a-user-centric-interface-for-data-scientists-in-the-cloud-age.json @@ -0,0 +1,43 @@ +{ + "description": "In this talk, we'll look into why Insee had to go beyond usual tools like JupyterHub. With data science growing, it has become important to have tools that are easy to use, can change as needed, and help people work together. The opensource software Onyxia brings a new answer by offering a user-friendly way to boost creativity in a data environment that uses massively containerization and object storage.\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": 1818, + "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/UFbOBz-Aw1I/sddefault.jpg", + "title": "Comte et al. - Onyxia: A User-Centric Interface for Data Scientists in the Cloud Age", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=UFbOBz-Aw1I" + } + ] +} diff --git a/pydata-paris-2024/videos/cordier-laurent-boosting-ai-reliability-uncertainty-quantification-with-mapie.json b/pydata-paris-2024/videos/cordier-laurent-boosting-ai-reliability-uncertainty-quantification-with-mapie.json new file mode 100644 index 000000000..474bf5834 --- /dev/null +++ b/pydata-paris-2024/videos/cordier-laurent-boosting-ai-reliability-uncertainty-quantification-with-mapie.json @@ -0,0 +1,47 @@ +{ + "description": "MAPIE (Model Agnostic Prediction Interval Estimator) is your go-to solution for managing uncertainties and risks in machine learning models. This Python library, nestled within scikit-learn-contrib, offers a way to calculate prediction intervals with controlled coverage rates for regression, classification, and even time series analysis. But it doesn't stop there - MAPIE can also be used to handle more complex tasks like multi-label classification and semantic segmentation in computer vision, ensuring probabilistic guarantees on crucial metrics like recall and precision. MAPIE can be integrated with any model - whether it's scikit-learn, TensorFlow, or PyTorch. Join us as we delve into the world of conformal predictions and how to quickly manage your uncertainties using MAPIE.\n\nLink to Github: https://github.com/scikit-learn-contrib/MAPIE\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": 1809, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://github.com/scikit-learn-contrib/MAPIE", + "url": "https://github.com/scikit-learn-contrib/MAPIE" + }, + { + "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/Rpy-Ozci_80/sddefault.jpg", + "title": "Cordier & Laurent - Boosting AI Reliability- Uncertainty Quantification with MAPIE", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Rpy-Ozci_80" + } + ] +} diff --git a/pydata-paris-2024/videos/david-brochart-collaborative-editing-in-jupyter-pydata-paris-2024.json b/pydata-paris-2024/videos/david-brochart-collaborative-editing-in-jupyter-pydata-paris-2024.json new file mode 100644 index 000000000..10ed81e33 --- /dev/null +++ b/pydata-paris-2024/videos/david-brochart-collaborative-editing-in-jupyter-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "The Jupyter stack has undergone a significant transformation in recent years with the integration of collaborative editing features: users can now modify a shared document and see each other's changes in real time, with a user experience akin to that of Google Docs. The underlying technology uses a special data structure called Conflict-free Replicated Data Types (CRDTs), that automatically resolves conflicts when concurrent changes are made. This allows data to be distributed rather than centralized in a server, letting clients work as if data was local rather than remote.\nIn this talk, we look at new possibilities that CRDTs can unlock, and how they are redefining Jupyter's architecture. Different use cases are presented: a suggestion system similar to Google Doc's, a chat system allowing collaboration with an AI agent, an execution model allowing full notebook state recovery, a collaborative widget model. We also look at the benefits of using CRDTs in JupyterLite, where users can interact without a server. This may be a great example of a distributed system where every user owns their data and shares them with their peers.\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": 1695, + "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/VXXLnmGqAO4/sddefault.jpg", + "title": "David Brochart - Collaborative editing in Jupyter | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=VXXLnmGqAO4" + } + ] +} diff --git a/pydata-paris-2024/videos/el-mawass-knorps-evaluating-the-evaluator-rag-eval-libraries-under-the-loop-pydata-paris-2024.json b/pydata-paris-2024/videos/el-mawass-knorps-evaluating-the-evaluator-rag-eval-libraries-under-the-loop-pydata-paris-2024.json new file mode 100644 index 000000000..8eb15622a --- /dev/null +++ b/pydata-paris-2024/videos/el-mawass-knorps-evaluating-the-evaluator-rag-eval-libraries-under-the-loop-pydata-paris-2024.json @@ -0,0 +1,47 @@ +{ + "description": "Retrieval-augmented generation (RAG) has become a key application for large language models (LLMs), enhancing their responses with information from external databases. However, RAG systems are prone to errors, and their complexity has made evaluation a critical and challenging area. Various libraries (like RAGAS and TruLens) have introduced evaluation tools and metrics for RAGs, but these evaluations involve using one LLM to assess another, raising questions about their reliability. Our study examines the stability and usefulness of these evaluation methods across different datasets and domains, focusing on the effects of the choice of the evaluation LLM, query reformulation, and dataset characteristics on RAG performance. It also assesses the stability of the metrics on multiple runs of the evaluation and how metrics correlate with each other. The talk aims to guide users in selecting and interpreting LLM-based evaluations effectively.\n\nhttps://github.com/tweag/pydata-paris-2024-slides\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": 1790, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://github.com/tweag/pydata-paris-2024-slides", + "url": "https://github.com/tweag/pydata-paris-2024-slides" + }, + { + "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/yrWmK3pXrKk/sddefault.jpg", + "title": "El Mawass & Knorps - Evaluating the evaluator- RAG eval libraries under the loop | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=yrWmK3pXrKk" + } + ] +} diff --git a/pydata-paris-2024/videos/emanuele-fabbiani-is-your-marketing-effective-let-bayes-decide-pydata-paris-2024.json b/pydata-paris-2024/videos/emanuele-fabbiani-is-your-marketing-effective-let-bayes-decide-pydata-paris-2024.json new file mode 100644 index 000000000..4307588ce --- /dev/null +++ b/pydata-paris-2024/videos/emanuele-fabbiani-is-your-marketing-effective-let-bayes-decide-pydata-paris-2024.json @@ -0,0 +1,51 @@ +{ + "description": "Understanding the effectiveness of various marketing channels is crucial to maximise the return on investment (ROI). However, the limitation of third-party cookies and an ever-growing focus on privacy make it difficult to rely on basic analytics. This talk discusses a pioneering project where a **Bayesian model** was employed to assess the marketing media mix effectiveness of **[WeRoad](https://www.weroad.it/)**, the fastest-growing Italian tour operator.\n\nThe Bayesian approach allows for the incorporation of prior knowledge, seamlessly updating it with new data to provide robust, actionable insights. This project leveraged a Bayesian model to unravel the complex interactions between marketing channels such as online ads, social media, and promotions. We'll dive deep into how the Bayesian model was designed, discussing how we provided the AI system with expert knowledge, and presenting how **delays and saturation** were modelled.\n\nWe will also tackle aspects of the technical implementation, discussing how Python, **[PyMC](https://www.pymc.io/welcome.html)**, and Streamlit provided us with the all the tools we needed to develop an effective, efficient, and user-friendly system.\n\nAttendees will walk away with:\n\n- A simple understanding of the Bayesian approach and why it matters.\n- Concrete examples of the transformative impact on WeRoad's marketing strategy.\n- A blueprint to harness predictive models in their business strategies.\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": 1771, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://www.pymc.io/welcome.html", + "url": "https://www.pymc.io/welcome.html" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://www.weroad.it/", + "url": "https://www.weroad.it/" + } + ], + "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/GYmE_VcAeJA/sddefault.jpg", + "title": "Emanuele Fabbiani - Is your marketing effective? Let Bayes decide! | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=GYmE_VcAeJA" + } + ] +} diff --git a/pydata-paris-2024/videos/erik-welch-fast-networkx-and-how-accelerated-backends-are-changing-graph-analytics.json b/pydata-paris-2024/videos/erik-welch-fast-networkx-and-how-accelerated-backends-are-changing-graph-analytics.json new file mode 100644 index 000000000..52d6236fa --- /dev/null +++ b/pydata-paris-2024/videos/erik-welch-fast-networkx-and-how-accelerated-backends-are-changing-graph-analytics.json @@ -0,0 +1,43 @@ +{ + "description": "NetworkX is arguably the most popular graph analytics library available today, but one of its greatest strengths - the pure-python implementation - is also possibly its biggest weakness. If you're a seasoned data scientists or a new student of the fascinating field of graph analytics, you're probably familiar with NetworkX and interested in how to make this extremely easy-to-use library powerful enough to handle realistically large graph workflows that often exceed the limitations of its pure-python implementation.\n\nThis talk will describe a relatively new capability of NetworkX; support for accelerated backends, and how they can benefit NetworkX users by allowing it to finally be both easy to use and fast. Through the use of backends, NetworkX can also be incorporated into workflows that take advantage of similar accelerators, such as Accelerated Pandas (cudf.pandas), to finally make these easy to use solutions scale to larger problems.\n\nAttend this talk to learn about how you can leverage the various backends available to NetworkX today to seamlessly run graph analytics on GPUs, use GraphBLAS implementations, and more, all without leaving the comfort and convenience of the most popular graph analytics library available.\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": 1838, + "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/Aqa8MaMGpEs/sddefault.jpg", + "title": "Erik Welch - Fast NetworkX and How Accelerated Backends Are Changing Graph Analytics", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Aqa8MaMGpEs" + } + ] +} diff --git a/pydata-paris-2024/videos/guillaume-desforges-processing-medical-images-at-scale-on-the-cloud-pydata-paris-2024.json b/pydata-paris-2024/videos/guillaume-desforges-processing-medical-images-at-scale-on-the-cloud-pydata-paris-2024.json new file mode 100644 index 000000000..938be6954 --- /dev/null +++ b/pydata-paris-2024/videos/guillaume-desforges-processing-medical-images-at-scale-on-the-cloud-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "The MedTech industry is undergoing a revolutionary transformation with continuous innovations promising greater precision, efficiency, and accessibility. In particular oncology, a branch of medicine that focuses on cancer, will benefit immensely from these new technologies, which may enable clinicians to detect cancer earlier and increase chances of survival. Detecting cancerous cells in microscopic photography of cells (Whole Slide Images, aka WSIs) is usually done with segmentation algorithms, which neural networks (NNs) are very good at. While using ML and NNs for image segmentation is a fairly standard task with established solutions, doing it on WSIs is a different kettle of fish. Most training pipelines and systems have been designed for analytics, meaning huge columns of small individual datums. In the case of WSIs, a single image is so huge that its file can be up to dozens of gigabytes. To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale.\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": 1675, + "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/GHTPLJpzgtM/sddefault.jpg", + "title": "Guillaume Desforges - Processing medical images at scale on the cloud | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=GHTPLJpzgtM" + } + ] +} diff --git a/pydata-paris-2024/videos/heidrich-kiraly-sktime-python-toolbox-for-time-series-next-generation-ai-pydata-paris-2024.json b/pydata-paris-2024/videos/heidrich-kiraly-sktime-python-toolbox-for-time-series-next-generation-ai-pydata-paris-2024.json new file mode 100644 index 000000000..48cf94b8c --- /dev/null +++ b/pydata-paris-2024/videos/heidrich-kiraly-sktime-python-toolbox-for-time-series-next-generation-ai-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "sktime is a widely used scikit-learn compatible library for learning with time series. sktime is easily extensible by anyone, and interoperable with the pydata/numfocus stack. \n\nThis talk presents progress, challenges, and newest features off the press, in extending the sktime framework to deep learning and foundation models.\n\nRecent progress in generative AI and deep learning is leading to an ever-exploding number of popular \u201cnext generation AI\u201d models for time series tasks like forecasting, classification, segmentation.\n\nParticular challenges of the new AI ecosystem are inconsistent formal interfaces, different deep learning backends, vendor specific APIs and architectures which do not match sklearn-like patterns well \u2013 every practitioner who has tried to use at least two such models at the same time (outside sktime) will have their individual painful memories.\n\nWe show how sktime brings its unified interface architecture for time series modelling to the brave new AI frontier, using novel design patterns building on ideas from hugging face and scikit-learn, to provide modular, extensible building blocks with a simple specification language.\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": 1826, + "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/gS3Sn-j_ooo/sddefault.jpg", + "title": "Heidrich & Kiraly - sktime: python toolbox for time series- next-generation AI | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=gS3Sn-j_ooo" + } + ] +} diff --git a/pydata-paris-2024/videos/hendrik-makait-geoscience-at-massive-scale-pydata-paris-2024.json b/pydata-paris-2024/videos/hendrik-makait-geoscience-at-massive-scale-pydata-paris-2024.json new file mode 100644 index 000000000..2bc989584 --- /dev/null +++ b/pydata-paris-2024/videos/hendrik-makait-geoscience-at-massive-scale-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "When scaling geoscience workloads to large datasets, many scientists and developers reach for Dask, a library for distributed computing that plugs seamlessly into Xarray and offers an Array API that wraps NumPy. Featuring a distributed environment capable of running your workload on large clusters, Dask promises to make it easy to scale from prototyping on your laptop to analyzing petabyte-scale datasets.\n\nDask has been the de-facto standard for scaling geoscience, but it hasn\u2019t entirely lived up to its promise of operating effortlessly at massive scale. This comes up in a few ways: \n- Correctly chunking your dataset has a significant impact on Dask\u2019s ability to scale \n- Workers accidentally run out of memory due to: \n - Data being loaded too eagerly\n - Rechunking\n - Unmanaged memory\n\nOver the last few months, Dask has addressed many of those pains and continues to do so through:\n- Improvements to its scheduling algorithms\n- A faster and more memory-stable method for rechunking\n- First-of-its-kind logical optimization layer for a distributed array framework (ongoing)\n\nJoin us as we dive into real-world geoscience workloads, exploring how Dask empowers scientists and developers to run their analyses at massive scale. Discover the impact of improvements made to Dask, ongoing challenges, and future plans for making it truly effortless to scale from your laptop to the cloud.\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": 1810, + "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/UOpNhItJNv8/sddefault.jpg", + "title": "Hendrik Makait - Geoscience at Massive Scale | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=UOpNhItJNv8" + } + ] +} diff --git a/pydata-paris-2024/videos/inacio-medeiros-unveiling-new-maps-of-biology-with-squidpy-pydata-paris-2024.json b/pydata-paris-2024/videos/inacio-medeiros-unveiling-new-maps-of-biology-with-squidpy-pydata-paris-2024.json new file mode 100644 index 000000000..947c60889 --- /dev/null +++ b/pydata-paris-2024/videos/inacio-medeiros-unveiling-new-maps-of-biology-with-squidpy-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "Spatial Transcriptomics, named the method of the year by Nature in 2020, offers remarkable visuals of gene expression across tissues and organs, providing valuable insights into biological processes. This talk presents the Squidpy library for analyzing and visualizing spatial molecular data, including demonstrations of gene expression visualization in mouse brain tissue.\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": 1752, + "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/qsYHuNcitzA/sddefault.jpg", + "title": "In\u00e1cio Medeiros - Unveiling new maps of biology with Squidpy | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=qsYHuNcitzA" + } + ] +} diff --git a/pydata-paris-2024/videos/jesse-averbukh-color-composite-images-from-the-james-webb-space-telescope.json b/pydata-paris-2024/videos/jesse-averbukh-color-composite-images-from-the-james-webb-space-telescope.json new file mode 100644 index 000000000..caea08806 --- /dev/null +++ b/pydata-paris-2024/videos/jesse-averbukh-color-composite-images-from-the-james-webb-space-telescope.json @@ -0,0 +1,43 @@ +{ + "description": "The astronomical community has built a good amount of software to visualize and analyze the images obtained with the James Webb Space Telescope (JWST). In this talk, I will present the open-source Python package Jdaviz. I will show you how to visualize publicly available JWST images and build the pretty color images that we have all seen in the media. Half the talk will be an introduction to JWST and Jdaviz and half will be a hands on session on a cloud platform (you will only need to create an account) or on your own machine (the package is available on PyPI).\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": 1587, + "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/nwcJfIhFLRE/sddefault.jpg", + "title": "Jesse Averbukh - Color-composite images from the James Webb Space Telescope", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=nwcJfIhFLRE" + } + ] +} diff --git a/pydata-paris-2024/videos/johan-mabille-julien-jerphanion-unveiling-mamba-2-0-the-future-of-fast-package-management.json b/pydata-paris-2024/videos/johan-mabille-julien-jerphanion-unveiling-mamba-2-0-the-future-of-fast-package-management.json new file mode 100644 index 000000000..789f257aa --- /dev/null +++ b/pydata-paris-2024/videos/johan-mabille-julien-jerphanion-unveiling-mamba-2-0-the-future-of-fast-package-management.json @@ -0,0 +1,43 @@ +{ + "description": "In this presentation, we introduce Mamba 2.0, the latest version of the multi-platform, language-agnostic package manager that has garnered significant adoption within the scientific open-source community for its speed and efficiency.\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": 1809, + "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/2Pu72HC1DW8/sddefault.jpg", + "title": "Johan Mabille & Julien Jerphanion - Unveiling Mamba 2.0: The Future of Fast Package Management", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=2Pu72HC1DW8" + } + ] +} diff --git a/pydata-paris-2024/videos/johannes-bechberger-python-3-12-s-new-monitoring-and-debugging-api-pydata-paris-2024.json b/pydata-paris-2024/videos/johannes-bechberger-python-3-12-s-new-monitoring-and-debugging-api-pydata-paris-2024.json new file mode 100644 index 000000000..20c99966a --- /dev/null +++ b/pydata-paris-2024/videos/johannes-bechberger-python-3-12-s-new-monitoring-and-debugging-api-pydata-paris-2024.json @@ -0,0 +1,47 @@ +{ + "description": "Python 3.12 introduced a new low-impact monitoring API with [PEP669](https://peps.python.org/pep-0669/), which can be used to implement far faster debuggers than ever before. This talk covers the main advantages of this API and how you can use it to develop small tools.\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": 1814, + "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" + }, + { + "label": "https://peps.python.org/pep-0669/", + "url": "https://peps.python.org/pep-0669/" + } + ], + "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/H90Sf5L_FHE/sddefault.jpg", + "title": "Johannes Bechberger - Python 3.12's new monitoring and debugging API | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=H90Sf5L_FHE" + } + ] +} diff --git a/pydata-paris-2024/videos/joris-van-den-bossche-the-expanding-apache-arrow-universe-pydata-paris-2024.json b/pydata-paris-2024/videos/joris-van-den-bossche-the-expanding-apache-arrow-universe-pydata-paris-2024.json new file mode 100644 index 000000000..d0acda127 --- /dev/null +++ b/pydata-paris-2024/videos/joris-van-den-bossche-the-expanding-apache-arrow-universe-pydata-paris-2024.json @@ -0,0 +1,47 @@ +{ + "description": "The expanding Apache Arrow universe - standardizing and accelerating tabular data access and interchange\n\nApache Arrow has become a de-facto standard for efficient in-memory columnar data representation. Beyond the standardized and language-independent columnar memory format for tabular data, the Apache Arrow project also has a growing set of supplementary specifications and language implementations. This talk will give an overview of the recent developments in the Apache Arrow ecosystem, including ADBC, nanoarrow, new data types, and the Arrow PyCapsule protocol.\n\nhttps://jorisvandenbossche.github.io/talks/2024_PyDataParis_Arrow/#1\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": 1893, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://jorisvandenbossche.github.io/talks/2024_PyDataParis_Arrow/#1", + "url": "https://jorisvandenbossche.github.io/talks/2024_PyDataParis_Arrow/#1" + }, + { + "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/3ehleDhnq18/sddefault.jpg", + "title": "Joris Van den Bossche - The expanding Apache Arrow universe | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=3ehleDhnq18" + } + ] +} diff --git a/pydata-paris-2024/videos/keynote-katharine-jarmul-diy-personalization-how-when-why-to-offer-self-made-ai.json b/pydata-paris-2024/videos/keynote-katharine-jarmul-diy-personalization-how-when-why-to-offer-self-made-ai.json new file mode 100644 index 000000000..8471bb163 --- /dev/null +++ b/pydata-paris-2024/videos/keynote-katharine-jarmul-diy-personalization-how-when-why-to-offer-self-made-ai.json @@ -0,0 +1,43 @@ +{ + "description": "With increased ease of smaller \"AI\" models, better chips and on-device learning, is it possible now to build and train your own models for your own use? In this keynote, we'll explore learnings of small, medium and large-sized model personalization, but driven by yourself and for yourself. A walk through what's possible, what's not and what we should prioritize if we'd like AI & ML to be made for everyone.\n\nKatharine Jarmul is a privacy activist and author of Practical Data Privacy (O'Reilly 2023). She has worked in privacy, security & ethics in machine learning & data for more than 10 years and rants about building things differently at probablyprivate.com\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": 2887, + "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/jYwe-YHM4ag/sddefault.jpg", + "title": "KEYNOTE: Katharine Jarmul - DIY Personalization: How, when why to offer self-made AI", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=jYwe-YHM4ag" + } + ] +} diff --git a/pydata-paris-2024/videos/keynote-merve-noyan-open-source-ai-why-it-matters-and-how-to-get-started-pydata-paris-2024.json b/pydata-paris-2024/videos/keynote-merve-noyan-open-source-ai-why-it-matters-and-how-to-get-started-pydata-paris-2024.json new file mode 100644 index 000000000..22273ddba --- /dev/null +++ b/pydata-paris-2024/videos/keynote-merve-noyan-open-source-ai-why-it-matters-and-how-to-get-started-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "In this talk, we will go through everything open-source AI: the state of open-source AI, why it matters, the future of it and how you can get started with it.\n\nMerve is a machine learning advocate engineer at Hugging Face. She enjoys contributing to open-source and researching about multimodality, foundation vision models and how to make them more accessible through optimization.\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": 2110, + "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/-Mv1KGr2FKs/sddefault.jpg", + "title": "KEYNOTE: Merve Noyan - Open-source AI: why it matters and how to get started | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=-Mv1KGr2FKs" + } + ] +} diff --git a/pydata-paris-2024/videos/keynote-olivier-grisel-handling-predictive-uncertainty-in-machine-learning-pydata-paris-2024.json b/pydata-paris-2024/videos/keynote-olivier-grisel-handling-predictive-uncertainty-in-machine-learning-pydata-paris-2024.json new file mode 100644 index 000000000..4e7d4bae0 --- /dev/null +++ b/pydata-paris-2024/videos/keynote-olivier-grisel-handling-predictive-uncertainty-in-machine-learning-pydata-paris-2024.json @@ -0,0 +1,47 @@ +{ + "description": "Machine Learning practitioners build predictive models from \"noisy\" data resulting in uncertain predictions. But what does \"noise\" mean in a machine learning context?\n\nOlivier is an Open Source Fellow at probabl and a core contributor to the scikit-learn machine learning library.\n\nhttps://docs.google.com/presentation/d/1EBCSCDQ3nTPaKZGx9ZLWXfvkD1Y-ODo9j_ETAnx5zLQ/edit#slide=id.g2f26b611275_1_45\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": 2667, + "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" + }, + { + "label": "https://docs.google.com/presentation/d/1EBCSCDQ3nTPaKZGx9ZLWXfvkD1Y-ODo9j_ETAnx5zLQ/edit#slide=id.g2f26b611275_1_45", + "url": "https://docs.google.com/presentation/d/1EBCSCDQ3nTPaKZGx9ZLWXfvkD1Y-ODo9j_ETAnx5zLQ/edit#slide=id.g2f26b611275_1_45" + } + ], + "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/-gYnfA0e5ic/sddefault.jpg", + "title": "KEYNOTE: Olivier Grisel - Handling predictive uncertainty in Machine Learning | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=-gYnfA0e5ic" + } + ] +} diff --git a/pydata-paris-2024/videos/keynote-sophia-yang-building-with-mistral-pydata-paris-2024.json b/pydata-paris-2024/videos/keynote-sophia-yang-building-with-mistral-pydata-paris-2024.json new file mode 100644 index 000000000..688197a1c --- /dev/null +++ b/pydata-paris-2024/videos/keynote-sophia-yang-building-with-mistral-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "In the rapidly evolving landscape of Artificial Intelligence (AI), open source and openness AI have emerged as crucial factors in fostering innovation, transparency, and accountability. Mistral AI's release of the open-weight Mistral 7B model has sparked significant adoption and demand, highlighting the importance of open-source and customization in building AI applications. This talk focuses on the Mistral AI model landscape, the benefits of open-source and customization, and the opportunities for building AI applications using Mistral models.\n\nSophia Yang is the Head of Developer Relations at Mistral AI, where she leads developer education, developer ecosystem partnerships, and community engagement. She is passionate about the AI community and the open-source community, and she is committed to empower community growth and learning.\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": 2424, + "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/kMklbMa7GZc/sddefault.jpg", + "title": "KEYNOTE: Sophia Yang - Building with Mistral | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=kMklbMa7GZc" + } + ] +} diff --git a/pydata-paris-2024/videos/klein-marti-escofet-an-introduction-to-metalearners-a-flexible-metalearner-library-in-python.json b/pydata-paris-2024/videos/klein-marti-escofet-an-introduction-to-metalearners-a-flexible-metalearner-library-in-python.json new file mode 100644 index 000000000..36545c4a4 --- /dev/null +++ b/pydata-paris-2024/videos/klein-marti-escofet-an-introduction-to-metalearners-a-flexible-metalearner-library-in-python.json @@ -0,0 +1,51 @@ +{ + "description": "Discover metalearners, a cutting-edge Python library designed for Causal Inference with particularly flexible and user-friendly MetaLearner implementations. metalearners leverages the power of conventional Machine Learning estimators and molds them into causal treatment effect estimators. \n\nThis talk is targeted towards data professionals with some Python and Machine Learning competences, guiding them to optimizing interventions such as 'Which potential customers should receive a voucher to optimally allocate a voucher budget?' or 'Which patients should receive which medical treatment?' based on causal interpretations.\n\nKevin Klein and Francesc Mart\u00ed Escofet are Data Scientists from QuantCo.\n\nhttps://github.com/kklein/pdp24-metalearners/blob/main/slides/slides.pdf\nhttps://github.com/Quantco/metalearners\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\n\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": 1934, + "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" + }, + { + "label": "https://github.com/Quantco/metalearners", + "url": "https://github.com/Quantco/metalearners" + }, + { + "label": "https://github.com/kklein/pdp24-metalearners/blob/main/slides/slides.pdf", + "url": "https://github.com/kklein/pdp24-metalearners/blob/main/slides/slides.pdf" + } + ], + "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/3EXCLYI5_pU/sddefault.jpg", + "title": "Klein & Mart\u00ed Escofet - An Introduction to 'metalearners', a Flexible MetaLearner Library in Python", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=3EXCLYI5_pU" + } + ] +} diff --git a/pydata-paris-2024/videos/lemaitre-senger-an-update-on-the-latest-scikit-learn-features-pydata-paris-2024.json b/pydata-paris-2024/videos/lemaitre-senger-an-update-on-the-latest-scikit-learn-features-pydata-paris-2024.json new file mode 100644 index 000000000..a25d8e3f5 --- /dev/null +++ b/pydata-paris-2024/videos/lemaitre-senger-an-update-on-the-latest-scikit-learn-features-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "In this talk, we provide an update on the latest `scikit-learn` features that have been implemented in versions 1.4 and 1.5. We will particularly discuss the following features:\n\n- the metadata routing API allowing to pass metadata around estimators;\n- the `TunedThresholdClassifierCV` allowing to tuned operational decision through custom metric;\n- better support for categorical features and missing values;\n- interoperability of array and dataframe.\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": 1852, + "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/PlCn-hqdMLw/sddefault.jpg", + "title": "Lemaitre & Senger - An update on the latest scikit-learn features | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=PlCn-hqdMLw" + } + ] +} diff --git a/pydata-paris-2024/videos/lightning-talks-session-1-pydata-paris-2024.json b/pydata-paris-2024/videos/lightning-talks-session-1-pydata-paris-2024.json new file mode 100644 index 000000000..94062589e --- /dev/null +++ b/pydata-paris-2024/videos/lightning-talks-session-1-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "www.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": 2827, + "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/B31X9nHZLe4/sddefault.jpg", + "title": "Lightning talks - session 1 | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=B31X9nHZLe4" + } + ] +} diff --git a/pydata-paris-2024/videos/lightning-talks-session-2-pydata-paris-2024.json b/pydata-paris-2024/videos/lightning-talks-session-2-pydata-paris-2024.json new file mode 100644 index 000000000..0dadc2b33 --- /dev/null +++ b/pydata-paris-2024/videos/lightning-talks-session-2-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "www.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": 3088, + "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/Z2agL6Dgako/sddefault.jpg", + "title": "Lightning talks - session 2 | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Z2agL6Dgako" + } + ] +} diff --git a/pydata-paris-2024/videos/luis-blanche-benoit-courty-track-your-code-s-co2-emissions-with-code-carbon-pydata-paris-2024.json b/pydata-paris-2024/videos/luis-blanche-benoit-courty-track-your-code-s-co2-emissions-with-code-carbon-pydata-paris-2024.json new file mode 100644 index 000000000..480f8e6bb --- /dev/null +++ b/pydata-paris-2024/videos/luis-blanche-benoit-courty-track-your-code-s-co2-emissions-with-code-carbon-pydata-paris-2024.json @@ -0,0 +1,51 @@ +{ + "description": "Rising concerns over IT's carbon footprint necessitate tools that gauge and mitigate these impacts. This session introduces CodeCarbon, an open-source tool that estimates computing's carbon emissions by measuring energy use across hardware components. Aimed at AI researchers and data scientists, CodeCarbon provides actionable insights into the environmental costs of computational projects, supporting efforts towards sustainability without requiring deep technical expertise.\n\nThis talk from the main contributors of Code Carbon will cover the environmental impact of IT, the possibilities to estimate it and a demo of CodeCarbon.\n\nhttps://www.linkedin.com/feed/update/urn:li:activity:7245529358763724803/\nhttps://github.com/mlco2/codecarbon\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": 1834, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://www.linkedin.com/feed/update/urn:li:activity:7245529358763724803/", + "url": "https://www.linkedin.com/feed/update/urn:li:activity:7245529358763724803/" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://github.com/mlco2/codecarbon", + "url": "https://github.com/mlco2/codecarbon" + } + ], + "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/z9jaloeED8Y/sddefault.jpg", + "title": "Luis Blanche & Beno\u00eet Courty - Track your code's CO2 emissions with Code Carbon | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=z9jaloeED8Y" + } + ] +} diff --git a/pydata-paris-2024/videos/m-knorps-z-zhang-on-the-structure-and-reproducibility-of-python-packages-data-crunch.json b/pydata-paris-2024/videos/m-knorps-z-zhang-on-the-structure-and-reproducibility-of-python-packages-data-crunch.json new file mode 100644 index 000000000..c95f5a4cb --- /dev/null +++ b/pydata-paris-2024/videos/m-knorps-z-zhang-on-the-structure-and-reproducibility-of-python-packages-data-crunch.json @@ -0,0 +1,43 @@ +{ + "description": "Did you know that all top PyPI packages declare their 3rd party dependencies? In contrast, only about 53% of scientific projects do the same. The question arises: How can we reproduce Python-based scientific experiments if we're unaware of the necessary libraries for our environment?\nIn this talk, we delve into the Python packaging ecosystem and employ a data-driven approach to analyze the structure and reproducibility of packages. We compare two distinct groups of Python packages: the most popular ones on PyPI, which we anticipate to adhere more closely to best practices, and a selection from biomedical experiments. Through our analysis, we uncover common development patterns in Python projects and utilize our open-source library, FawltyDeps, to identify undeclared dependencies and assess the reproducibility of these projects.\nThis discussion is especially valuable for enthusiasts of clean Python code, as well as for data scientists and engineers eager to adopt best practices and enhance reproducibility. Attendees will depart with actionable insights on enhancing the transparency and reliability of their Python projects, thereby advancing the cause of reproducible scientific 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": 2076, + "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/PB4NTTxYExs/sddefault.jpg", + "title": "M. Knorps & Z. Zhang - On the structure and reproducibility of Python packages - data crunch", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=PB4NTTxYExs" + } + ] +} diff --git a/pydata-paris-2024/videos/marco-gorelli-polars-plugins-how-you-yes-you-can-extend-polars-dataframes-pydata-paris-2024.json b/pydata-paris-2024/videos/marco-gorelli-polars-plugins-how-you-yes-you-can-extend-polars-dataframes-pydata-paris-2024.json new file mode 100644 index 000000000..966cb1c26 --- /dev/null +++ b/pydata-paris-2024/videos/marco-gorelli-polars-plugins-how-you-yes-you-can-extend-polars-dataframes-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "Polars is a dataframe library taking the world by storm. It is very runtime and memory efficient and comes with a clean and expressive API. Sometimes, however, the built-in API isn't enough. And that's where its killer feature comes in: plugins. You can extend Polars, and solve practically any problem.\n\nNo prior Rust experience required, intermediate Python and programming experience required. By the end of the talk, you will know how to write your own Polars Plugin! This talk is aimed at data practitioners.\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": 1849, + "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/8Ex93IG37VI/sddefault.jpg", + "title": "Marco Gorelli - Polars Plugins: how you (yes, you!) can extend Polars Dataframes | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=8Ex93IG37VI" + } + ] +} diff --git a/pydata-paris-2024/videos/max-halford-unpack-business-metrics-to-explain-their-evolution-pydata-paris-2024.json b/pydata-paris-2024/videos/max-halford-unpack-business-metrics-to-explain-their-evolution-pydata-paris-2024.json new file mode 100644 index 000000000..32f9ea6c2 --- /dev/null +++ b/pydata-paris-2024/videos/max-halford-unpack-business-metrics-to-explain-their-evolution-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "One of the more mundane tasks in the business analytics world is to measure KPIs: averages, sums, ratios, etc. Typically, these are measured period over period, to see how they trend. If you're a data analyst, you've likely been asked to debug/explain a metric, because a stakeholder wants to understand why a number has changed.\n\nThis topic isn't well grounded theory, and the answers we come up with can be lacklustre. In this talk, we discuss solutions to this very common topic. We will look at a methodology we have developed at Carbonfact, and the opensource Python tool we are sharing.\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": 2086, + "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/kiWW4Oty8kY/sddefault.jpg", + "title": "Max Halford - Unpack business metrics to explain their evolution | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=kiWW4Oty8kY" + } + ] +} diff --git a/pydata-paris-2024/videos/nichita-morcotilo-bridging-the-worlds-pixi-reimplements-pip-and-conda-in-rust.json b/pydata-paris-2024/videos/nichita-morcotilo-bridging-the-worlds-pixi-reimplements-pip-and-conda-in-rust.json new file mode 100644 index 000000000..2e5c5f37f --- /dev/null +++ b/pydata-paris-2024/videos/nichita-morcotilo-bridging-the-worlds-pixi-reimplements-pip-and-conda-in-rust.json @@ -0,0 +1,59 @@ +{ + "description": "Pixi goes further than existing conda-based package managers in many ways:\n\n* From scratch implemented in Rust and ships as a single binary\n* Integrates a new SAT solver called resolvo\n* Supports lockfiles like `poetry` / `yarn` / `cargo` do\n* Cross-platform task system (simple `bash`-like syntax)\n* Interoperability with PyPI packages by integrating `uv`\n* It's 100% open-source with a permissive licence\n\nWe\u2019re looking forward to take a deep-dive together into what conda and PyPI packages are and how we are seamlessly integrating the two worlds in pixi.\n\nWe will show you how you can easily setup your new project using just one configuration file and always have a reproducible setup in your pocket. Which means that it will always run the same for your contributors, user and CI machine ( no more \"\"but it worked on my machine!\"\" ).\n\nUsing pixi's powerful cross-platform task system you can replace your `Makefile` and a ton of developer documentation with just `pixi run task`!\n\nWe\u2019ll also look at benchmarks and explain more about the difference between the conda and pypi ecosystems.\n\nThis talk is for everyone who ever dealt with [dependency hell](https://prefix.dev/blog/launching_pixi).\n\nMore information about Pixi:\n\nhttps://pixi.sh\nhttps://prefix.dev\nhttps://github.com/prefix-dev/pixi\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": 1755, + "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" + }, + { + "label": "https://pixi.sh", + "url": "https://pixi.sh" + }, + { + "label": "https://github.com/prefix-dev/pixi", + "url": "https://github.com/prefix-dev/pixi" + }, + { + "label": "https://prefix.dev/blog/launching_pixi", + "url": "https://prefix.dev/blog/launching_pixi" + }, + { + "label": "https://prefix.dev", + "url": "https://prefix.dev" + } + ], + "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/O-up045PgXE/sddefault.jpg", + "title": "Nichita Morcotilo - Bridging the worlds: pixi reimplements pip and conda in Rust", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=O-up045PgXE" + } + ] +} diff --git a/pydata-paris-2024/videos/nicolas-guenon-des-mesnards-would-you-rely-on-chatgpt-to-dial-911-pydata-paris-2024.json b/pydata-paris-2024/videos/nicolas-guenon-des-mesnards-would-you-rely-on-chatgpt-to-dial-911-pydata-paris-2024.json new file mode 100644 index 000000000..0281d202c --- /dev/null +++ b/pydata-paris-2024/videos/nicolas-guenon-des-mesnards-would-you-rely-on-chatgpt-to-dial-911-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "Would you rely on ChatGPT to dial 911? A talk on balancing determinism and probabilism in production machine learning systems\n\nIn the last year there hasn\u2019t been a day that passed without us hearing about a new generative AI innovation that will enhance some aspect of our lives. On a number of tasks large probabilistic systems are now outperforming humans, or at least they do so \u201con average\u201d. \u201cOn average\u201d means most of the time, but in many real life scenarios \u201caverage\u201d performance is not enough: we need correctness ALL of the time, for example when you ask the system to dial 911. \n\nIn this talk we will explore the synergy between deterministic and probabilistic models to enhance the robustness and controllability of machine learning systems. Tailored for ML engineers, data scientists, and researchers, the presentation delves into the necessity of using both deterministic algorithms and probabilistic model types across various ML systems, from straightforward classification to advanced Generative AI models. \n\nYou will learn about the unique advantages each paradigm offers and gain insights into how to most effectively combine them for optimal performance in real-world applications. I will walk you through my past and current experiences in working with simple and complex NLP models, and show you what kind of pitfalls, shortcuts, and tricks are possible to deliver models that are both competent and reliable.\n\nThe session will be structured into a brief introduction to both model types, followed by case studies in classification and generative AI, concluding with a Q&A segment.\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": 1727, + "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/UUNaRYvEJkY/sddefault.jpg", + "title": "Nicolas Guenon des Mesnards - Would you rely on ChatGPT to dial 911? | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=UUNaRYvEJkY" + } + ] +} diff --git a/pydata-paris-2024/videos/nicolas-m-thiery-jupylates-spaced-repetition-for-teaching-with-jupyter.json b/pydata-paris-2024/videos/nicolas-m-thiery-jupylates-spaced-repetition-for-teaching-with-jupyter.json new file mode 100644 index 000000000..6b769e08d --- /dev/null +++ b/pydata-paris-2024/videos/nicolas-m-thiery-jupylates-spaced-repetition-for-teaching-with-jupyter.json @@ -0,0 +1,55 @@ +{ + "description": "Jupyter based environments are getting a lot of traction for teaching computing, programming, and data sciences. The narrative structure of notebooks has indeed proven its value for guiding each student at it's own pace to the discovery and understanding of new concepts or new idioms (e.g. how do I extract a column in pandas?). But then these new pieces of knowledge tend to quickly fade out and be forgotten. Indeed long term acquisition of knowledge and skills takes reinforcement by repetition. This is the foundation of many online learning platforms like [Webwork](https://en.wikipedia.org/wiki/WeBWorK) or [WIMS](https://fr.wikipedia.org/wiki/WIMS) that offer exercises with randomization and automatic feedback. And of popular \"\"AI-powered\"\" apps -- e.g. to learn foreign languages -- that use spaced repetition algorithms designed by educational and neuro sciences to deliver just the right amount of repetition.\n\nWhat if you could author such exercizes as notebooks, to benefit from everything that Jupyter can offer (think rich narratives, computations, visualization, interactions)? What if you could integrate such exercises right into your Jupyter based course? What if a learner could get personalized exercise recommandations based on their past learning records, without having to give away these sensitive pieces of information away?\n\nThat's [Jupylates](https://gitlab.dsi.universite-paris-saclay.fr/jupyter/jupylates/) (work in progress). And thanks to the open source scientific stack, it's just a small Jupyter extension.\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": 1901, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://fr.wikipedia.org/wiki/WIMS", + "url": "https://fr.wikipedia.org/wiki/WIMS" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://gitlab.dsi.universite-paris-saclay.fr/jupyter/jupylates/", + "url": "https://gitlab.dsi.universite-paris-saclay.fr/jupyter/jupylates/" + }, + { + "label": "https://en.wikipedia.org/wiki/WeBWorK", + "url": "https://en.wikipedia.org/wiki/WeBWorK" + } + ], + "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/4WDVm4PcHig/sddefault.jpg", + "title": "Nicolas M. Thi\u00e9ry - Jupylates- spaced repetition for teaching with Jupyter", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=4WDVm4PcHig" + } + ] +} diff --git a/pydata-paris-2024/videos/noe-achache-towards-a-deeper-understanding-of-retrieval-and-vector-databases-pydata-paris-2024.json b/pydata-paris-2024/videos/noe-achache-towards-a-deeper-understanding-of-retrieval-and-vector-databases-pydata-paris-2024.json new file mode 100644 index 000000000..7b716d6b1 --- /dev/null +++ b/pydata-paris-2024/videos/noe-achache-towards-a-deeper-understanding-of-retrieval-and-vector-databases-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "Retrieval is the process of searching for a given item (image, text, \u2026) in a large database that are similar to one or more query items. A classical approach is to transform the database items and the query item into vectors (also called embeddings) with a trained model so that they can be compared via a distance metric. It has many applications in various fields, e.g. to build a visual recommendation system like Google Lens or a RAG (Retrieval Augmented Generation), a technique used to inject specific knowledge into LLMs depending on the query. \nVector databases ease the management, serving and retrieval of the vectors in production and implement efficient indexes, to rapidly search through millions of vectors. They gained a lot of attention over the past year, due to the rise of LLMs and RAGs.\n\nAlthough people working with LLMs are increasingly familiar with the basic principles of vector databases, the finer details and nuances often remain obscure. This lack of clarity hinders the ability to make optimal use of these systems.\n\nIn this talk, we will detail two examples of real-life projects (Deduplication of real estate adverts using the image embedding model DinoV2 and RAG for a medical company using the text embedding model Ada-2) and deep dive into retrieval and vector databases to demystify the key aspects and highlight the limitations: HSNW index, comparison of the providers, metadata filtering (the related plunge of performance when filtering too many nodes and how indexing partially helps it), partitioning, reciprocal rank fusion, the performance and limitations of the representations created by SOTA image and text embedding models, \u2026\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": 1953, + "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/FCAbLAFzNDE/sddefault.jpg", + "title": "No\u00e9 Achache - Towards a deeper understanding of retrieval and vector databases | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=FCAbLAFzNDE" + } + ] +} diff --git a/pydata-paris-2024/videos/patrick-hoefler-building-large-scale-etl-pipelines-with-dask-pydata-paris-2024.json b/pydata-paris-2024/videos/patrick-hoefler-building-large-scale-etl-pipelines-with-dask-pydata-paris-2024.json new file mode 100644 index 000000000..a88eba0d3 --- /dev/null +++ b/pydata-paris-2024/videos/patrick-hoefler-building-large-scale-etl-pipelines-with-dask-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "Building scalable ETL pipelines and deploying them in the cloud can seem daunting. It shouldn't be. Leveraging proper technologies can make this process easy. We will discuss the whole process of developing a composable and scalable ETL pipeline centred around Dask that is fully built with Open Source tools and how we can deploy to the cloud.\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": 1739, + "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/oZM4k_vVzMM/sddefault.jpg", + "title": "Patrick Hoefler - Building Large Scale ETL Pipelines with Dask | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=oZM4k_vVzMM" + } + ] +} diff --git a/pydata-paris-2024/videos/pierre-raybaut-bridging-scientific-and-industrial-worlds-for-advanced-signal-and-image-processing.json b/pydata-paris-2024/videos/pierre-raybaut-bridging-scientific-and-industrial-worlds-for-advanced-signal-and-image-processing.json new file mode 100644 index 000000000..db1af1a7e --- /dev/null +++ b/pydata-paris-2024/videos/pierre-raybaut-bridging-scientific-and-industrial-worlds-for-advanced-signal-and-image-processing.json @@ -0,0 +1,51 @@ +{ + "description": "This talk introduces [DataLab](https://datalab-platform.com), a unique open-source platform for signal and image processing, seamlessly integrating scientific and industrial applications.\n\nThe main objective of this talk is to show how [DataLab](https://datalab-platform.com) may be used as a complementary tool alongside with Jupyter notebooks or an IDE (e.g., [Spyder](https://www.spyder-ide.org/)), and how it can be extended with custom Python scripts or applications.\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": 1866, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://datalab-platform.com", + "url": "https://datalab-platform.com" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://www.spyder-ide.org/", + "url": "https://www.spyder-ide.org/" + } + ], + "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/yn1bR-BVfn8/sddefault.jpg", + "title": "Pierre Raybaut - Bridging Scientific and Industrial Worlds for Advanced Signal and Image Processing", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=yn1bR-BVfn8" + } + ] +} diff --git a/pydata-paris-2024/videos/serge-sans-paille-xsimd-from-xtensor-to-firefox-pydata-paris-2024.json b/pydata-paris-2024/videos/serge-sans-paille-xsimd-from-xtensor-to-firefox-pydata-paris-2024.json new file mode 100644 index 000000000..4f52da6a4 --- /dev/null +++ b/pydata-paris-2024/videos/serge-sans-paille-xsimd-from-xtensor-to-firefox-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "Almost all modern CPU have a vector processing unit, making it possible to write faster code for a large category of problems, at the cost of portability - there a re many different instruction sets in the wild! The xsimd library makes it possible to write portable C++ code that targets different architectures and sub-architectures. The specialization choice can be made at compile-time or at runtime, using a provided dispatching mechanism. Intel, ARM, RiscV and Webassembly are supported, and the library has already been adopted by Xtensor, Pythran, Apache Arrow and Firefox.\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": 1972, + "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/tgdiKd3XXSQ/sddefault.jpg", + "title": "Serge \u00ab sans \u00bb Paille - xsimd: from xtensor to firefox | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=tgdiKd3XXSQ" + } + ] +} diff --git a/pydata-paris-2024/videos/silva-enhancing-rag-based-apps-by-constructing-and-leveraging-knowledge-graphs-with-open-source-llms.json b/pydata-paris-2024/videos/silva-enhancing-rag-based-apps-by-constructing-and-leveraging-knowledge-graphs-with-open-source-llms.json new file mode 100644 index 000000000..3a53a7540 --- /dev/null +++ b/pydata-paris-2024/videos/silva-enhancing-rag-based-apps-by-constructing-and-leveraging-knowledge-graphs-with-open-source-llms.json @@ -0,0 +1,51 @@ +{ + "description": "Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at representing and storing heterogeneous and interconnected information in a structured manner, effortlessly capturing complex relationships and attributes across different data types. Using open weights LLMs removes the dependency on an external LLM provider while retaining complete control over the data flows and how the data is being shared and stored. In this talk, we construct and leverage the structured nature of graph databases, which organize data as nodes and relationships, to enhance the depth and contextuality of retrieved information to enhance RAG-based applications with open weights LLMs. We will show these capabilities with a demo.\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\nhttps://alonsosilvaallende.github.io/2024-PyData-Paris/#/title-slide\nhttps://github.com/alonsosilvaallende/2024-PyData-Paris/blob/main/PyData-Paris.ipynb\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": 1838, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://github.com/alonsosilvaallende/2024-PyData-Paris/blob/main/PyData-Paris.ipynb", + "url": "https://github.com/alonsosilvaallende/2024-PyData-Paris/blob/main/PyData-Paris.ipynb" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://alonsosilvaallende.github.io/2024-PyData-Paris/#/title-slide", + "url": "https://alonsosilvaallende.github.io/2024-PyData-Paris/#/title-slide" + } + ], + "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/sjprfQw5TJw/sddefault.jpg", + "title": "Silva-Enhancing RAG-based apps by constructing and leveraging knowledge graphs with open-source LLMs", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=sjprfQw5TJw" + } + ] +} diff --git a/pydata-paris-2024/videos/simeon-carstens-chainsai-facilitating-sampling-of-multimodal-probability-distributions.json b/pydata-paris-2024/videos/simeon-carstens-chainsai-facilitating-sampling-of-multimodal-probability-distributions.json new file mode 100644 index 000000000..9391845fe --- /dev/null +++ b/pydata-paris-2024/videos/simeon-carstens-chainsai-facilitating-sampling-of-multimodal-probability-distributions.json @@ -0,0 +1,43 @@ +{ + "description": "Markov chain Monte Carlo (MCMC) methods, a class of iterative algorithms that allow sampling almost arbitrary probability distributions, have become increasingly popular and accessible to statisticians and scientists. But they run into difficulties when applied to multimodal probability distributions. These occur, for example, in Bayesian data analysis, when multiple regions in the parameter space explain the data equally well or when some parameters are redundant. Inaccurate sampling then results in incomplete and misleading parameter estimates.\nMarkov chain Monte Carlo (MCMC) methods, a very popular class of iterative algorithms that allow sampling almost arbitrary probability distributions, run into difficulties when applied to multimodal probability distributions. These occur, for example, in Bayesian data analysis, when multiple regions in the parameter space explain the data equally well or when some parameters are redundant.\nIn this talk, intended for data scientists and statisticians with basic knowledge of MCMC and probabilistic programming, I present Chainsail, an open-source web service written entirely in Python. It implements Replica Exchange, an advanced MCMC method designed specifically to improve sampling of multimodal distributions.\nChainsail makes this algorithm easily accessible to users of probabilistic programming libraries by automatically tuning important parameters and exploiting easy on-demand provisioning of the (increased) computing resources necessary for running Replica Exchange.\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": 1849, + "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/PzyM72tVBt8/sddefault.jpg", + "title": "Simeon Carstens - Chainsai:- facilitating sampling of multimodal probability distributions", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=PzyM72tVBt8" + } + ] +} diff --git a/pydata-paris-2024/videos/tim-paine-high-performance-data-visualization-for-the-web-pydata-paris-2024.json b/pydata-paris-2024/videos/tim-paine-high-performance-data-visualization-for-the-web-pydata-paris-2024.json new file mode 100644 index 000000000..8b1b1ee8f --- /dev/null +++ b/pydata-paris-2024/videos/tim-paine-high-performance-data-visualization-for-the-web-pydata-paris-2024.json @@ -0,0 +1,43 @@ +{ + "description": "Are you looking for a high performance visualization component for the web? Need to filter, sort, pivot, and aggregate static/streaming data in realtime? Daunted by the massive JS ecosystem? In this talk, we\u2019ll build a high performance web frontend using the open source library Perspective.\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": 1667, + "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/myHfMhgIOEA/sddefault.jpg", + "title": "Tim Paine - High Performance Data Visualization for the Web | PyData Paris 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=myHfMhgIOEA" + } + ] +} diff --git a/pydata-paris-2024/videos/trung-le-martin-renou-building-web-based-engineering-applications-with-jupyterlab-components.json b/pydata-paris-2024/videos/trung-le-martin-renou-building-web-based-engineering-applications-with-jupyterlab-components.json new file mode 100644 index 000000000..f6424afb4 --- /dev/null +++ b/pydata-paris-2024/videos/trung-le-martin-renou-building-web-based-engineering-applications-with-jupyterlab-components.json @@ -0,0 +1,43 @@ +{ + "description": "In the past few years, web-based engineering software has been steadily gaining momentum over traditional desktop-based applications. It represents a significant shift in how engineers access, collaborate, and utilize software tools for design, analysis, and simulation tasks. However, converting desktop-based applications to web applications presents considerable challenges, especially in translating the functionality of desktop interfaces to the web. It requires careful planning and design expertise to ensure intuitive navigation and responsiveness.\n\nJupyterLab provides a flexible, interactive environment for scientific computing. Despite its popularity among data scientists and researchers, the full potential of JupyterLab as a platform for building scientific web applications has yet to be realized.\n\nIn this talk, we will explore how its modular architecture and extensive ecosystem facilitate the seamless integration of components for diverse functionalities: from rich user interfaces, accessibility, and real-time collaboration to cloud deployment options. To illustrate the platform's capabilities, we will demo JupyterCAD, a parametric 3D modeler built on top of JupyterLab components.\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": 1599, + "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/XIOFlo8_9ws/sddefault.jpg", + "title": "Trung Le & Martin Renou - Building web-based engineering applications with JupyterLab components", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=XIOFlo8_9ws" + } + ] +} diff --git a/pydata-paris-2024/videos/tuloup-thomas-jupyterlite-emscripten-forge-xeus-and-mamba.json b/pydata-paris-2024/videos/tuloup-thomas-jupyterlite-emscripten-forge-xeus-and-mamba.json new file mode 100644 index 000000000..76fa5afcf --- /dev/null +++ b/pydata-paris-2024/videos/tuloup-thomas-jupyterlite-emscripten-forge-xeus-and-mamba.json @@ -0,0 +1,51 @@ +{ + "description": "JupyterLite is a JupyterLab distribution that runs entirely in the web browser, backed by in-browser language kernels. With standard JupyterLab, where kernels run in separate processes and communicate with the client by message passing, JupyterLite uses kernels that run entirely in the browser, based on JavaScript and WebAssembly. \n\nThis means JupyterLite deployments can be scaled to millions of users without the need for individual containers for each user session, only static files need to be served, which can be done with a simple web server like GitHub pages.\n\nThis opens up new possibilities for large-scale deployments, eliminating the need for complex cloud computing infrastructure. JupyterLite is versatile and supports a wide range of languages, with the majority of its kernels implemented using Xeus, a C++ library for developing language-specific kernels.\n\nIn conjunction with JupyterLite, we present Emscripten-forge, a conda/mamba based distribution for WebAssembly packages. Conda-forge is a community effort and a GitHub organization which contains repositories of conda recipes and thus provides conda packages for a wide range of software and platforms. However, targeting WebAssembly is not supported by conda-forge. Emscripten-forge addresses this gap by providing conda packages for WebAssembly, making it possible to create custom JupyterLite deployments with tailored conda environments containing the required kernels and packages.\n\nIn this talk, we delve deep into the JupyterLite ecosystem, exploring its integration with Xeus Mamba and Emscripten-forge. \n\nWe will demonstrate how this can be used to create sophisticated JupyterLite deployments with custom conda environments and give an outlook for future developments like R packages and runtime package resolution.\n\nhttps://jtp.io/pydata-paris-2024-jupyterlite-xeus/files/index.html\nhttps://github.com/jtpio/pydata-paris-2024-jupyterlite-xeus\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": 1881, + "language": "eng", + "recorded": "2024-09-25", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/paris2024" + }, + { + "label": "https://github.com/jtpio/pydata-paris-2024-jupyterlite-xeus", + "url": "https://github.com/jtpio/pydata-paris-2024-jupyterlite-xeus" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://jtp.io/pydata-paris-2024-jupyterlite-xeus/files/index.html", + "url": "https://jtp.io/pydata-paris-2024-jupyterlite-xeus/files/index.html" + } + ], + "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/BNycVZk8TXc/sddefault.jpg", + "title": "Tuloup & Thomas - JupyterLite, Emscripten-forge, Xeus, and Mamba", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=BNycVZk8TXc" + } + ] +} diff --git a/pydata-paris-2024/videos/wright-dave-open-source-sustainability-philanthropy-building-contributor-communities.json b/pydata-paris-2024/videos/wright-dave-open-source-sustainability-philanthropy-building-contributor-communities.json new file mode 100644 index 000000000..5393c21c4 --- /dev/null +++ b/pydata-paris-2024/videos/wright-dave-open-source-sustainability-philanthropy-building-contributor-communities.json @@ -0,0 +1,43 @@ +{ + "description": "Open Source Software, the backbone of today\u2019s digital infrastructure, must be sustainable for the long-term. Qureshi and Fang (2011) find that motivating, engaging, and retaining new contributors is what makes open source projects sustainable.\n\nYet, as Steinmacher, et al. (2015) identifies, first-time open source contributors often lack timely answers to questions, newcomer orientation, mentors, and clear documentation. Moreover, since the term was first coined in 1998, open source lags far behind other technical domains in participant diversity. Trinkenreich, et al. (2022) reports that only about 5% of projects were reported to have women as core developers, and women authored less than 5% of pull requests, but had similar or even higher rates of pull request acceptances to men. So, how can we achieve more diversity in open source communities and projects?\n\nBloomberg\u2019s Women in Technology (BWIT) community, Open Source Program Office (OSPO), and Corporate Philanthropy team collaborated with NumFOCUS to develop a volunteer incentive model that aligns business value, philanthropic impact, and individual technical growth. Through it, participating Bloomberg engineers were given the opportunity to convert their hours spent contributing to the pandas open source project into a charitable donation to a non-profit of their choice.\n\nThe presenters will discuss how we wove together differing viewpoints: non-profit foundation and for-profit corporation, corporate philanthropy and engineers, first-time contributors and core devs. They will showcase why and how we converted technical contributions into charitable dollars, the difference this community-building model had in terms of creating a diverse and sustained group of new open source contributors, and the viability of extending this to other open source projects and corporate partners to contribute to the long-term sustainability of open source\u2014thereby demonstrating the true convergence of tech and social impact.\n\nNOTE: \n[1] Qureshi, I, and Fang, Y. \"\"Socialization in open source software projects: A growth mixture modeling approach.\"\" 2011.\n[2] Steinmacher, I., et al. \"\"Social barriers faced by newcomers placing their first contribution in open source software projects.\"\" 2015.\n[3] Trinkenreich, B., et al. \"\"Women\u2019s participation in open source software: A survey of the literature.\"\" 2022.\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": 1982, + "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/6tYLW3fQYVg/sddefault.jpg", + "title": "Wright & Dave - Open Source Sustainability & Philanthropy: Building Contributor Communities", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=6tYLW3fQYVg" + } + ] +}