Heidegger, Philosophy, and Politics : The Heidelberg Conference / / Philippe Lacoue-Labarthe, Hans-Georg Gadamer, Jacques Derrida; Mireille Calle-Gruber

• Record Nr.: UNINA9910965272403321
• Autore: Derrida Jacques
• Pubbl/distr/stampa: New York, NY : , : Fordham University Press, , [2016]; ©2016
• ISBN: 9780823273690; 0823273695
• Edizione: [First edition.]
• Descrizione fisica: 1 online resource (116 p.)
• Altri autori (Persone): FortJeff; NancyJean-Luc
• Disciplina: 193
• Soggetti: Derrida; French philosophy; Gadamer; Heidegger and nazism; Heidegger; Lacoue-Labarthe; philosophy and politics; LITERARY CRITICISM / Semiotics & Theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Frontmatter -- Contents -- Foreword -- Preface -- Event of the archive -- Conference of February 5, 1988 -- Meeting of February 6, 1988 -- Appendix: “like Plato in Syracuse” -- Notes
• Sommario/riassunto: In February 1988, philosophers Jacques Derrida, Hans-Georg Gadamer, and Philippe Lacoue-Labarthe came together in Heidelberg before a large audience to discuss the philosophical and political implications of Martin Heidegger’s thought. This event took place in the very amphitheater in which, more than fifty years earlier, Heidegger, as rector of the University of Freiburg and a member of the Nazi Party, had given a speech entitled “The University in the New Reich.” Heidegger’s involvement in Nazism has always been, and will remain, an indelible ; scandal, but what is its real relation to his work and thought? And what are the responsibilities of those who read this work, who analyze and elaborate this thought? Conversely, what is at stake in the wholesale dismissal of this important but compromised twentieth-century philosopher?In 1988, in the wake of the recent publication of Victor Farias’s Heidegger and Nazism, and of the heated debates that ensued, these questions had become more pressing than ever. The reflections presented by three of the most prominent of Heidegger’s readers, improvised in French and transcribed here, were an attempt to approach these questions before a broad public, but with a depth of knowledge and a complex sense of the questions at issue that have been often lacking in the press. Ranging over two days and including exchanges with one another and with the audience, the discussions pursued by these major thinkers remain highly relevant today, especially following the publication of Heidegger’s already notorious “Black Notebooks,” which have added another chapter to the ongoing debates over this contested figure. The present volume recalls a highly charged moment in this history, while also drawing the debate toward its most essential questions.

Data Visualization in R and Python

• Record Nr.: UNINA9911020334703321
• Autore: Cremonini Marco
• Pubbl/distr/stampa: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• ISBN: 9781394289516; 1394289510; 9781394289493; 1394289499; 9781394289509; 1394289502
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (578 pages)
• Disciplina: 005.75/65
• Soggetti: Visualization - Data processing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Introduction -- About the Companion Website -- Part I Static Graphics with ggplot (R) and Seaborn (Python) -- Chapter 1 Scatterplots and Line Plots -- 1.1 R: ggplot -- 1.1.1 Scatterplot -- 1.1.2 Repulsive Textual Annotations: Package ggrepel -- 1.1.3 Scatterplots with High Number of Data Points -- 1.1.4 Line Plot -- 1.2 Python: Seaborn -- 1.2.1 Scatterplot -- 1.2.2 Line Plot -- Chapter 2 Bar Plots -- 2.1 R: ggplot -- 2.1.1 Bar Plot and Continuous Variables: Ranges of Values -- 2.2 Python: Seaborn -- 2.2.1 Bar Plot with Three Variables -- 2.2.2 Ranges of Values from a Continuous Variable -- 2.2.3 Visualizing Subplots -- Chapter 3 Facets -- 3.1 R: ggplot -- 3.1.1 Case 1: Temperature -- 3.1.2 Case 2: Air Quality -- 3.2 Python: Seaborn -- 3.2.1 Facet for Scatterplots and Line Plot -- 3.2.2 Line Plot -- 3.2.3 Facet and Graphics for Categorical Variables -- 3.2.4 Facet and Bar Plots -- 3.2.5 Facets: General Method -- Chapter 4 Histograms and Kernel Density Plots -- 4.1 R: ggplot -- 4.1.1 Univariate Analysis -- 4.1.2 Bivariate Analysis -- 4.1.3 Kernel Density Plots -- 4.2 Python: Seaborn -- 4.2.1 Univariate Analysis -- 4.2.2 Bivariate Analysis -- 4.2.3 Logarithmic Scale -- Chapter 5 Diverging Bar Plots and Lollipop Plots -- 5.1 R: ggplot -- ; 5.1.1 Diverging Bar Plot -- 5.1.2 Lollipop Plot -- 5.2 Python: Seaborn -- 5.2.1 Diverging Bar Plot -- Chapter 6 Boxplots -- 6.1 R: ggplot -- 6.2 Python: Seaborn -- Chapter 7 Violin Plots -- 7.1 R: ggplot -- 7.1.1 Violin Plot and Scatterplot -- 7.1.2 Violin Plot and Boxplot -- 7.2 Python: Seaborn -- Chapter 8 Overplotting, Jitter, and Sina Plots -- 8.1 Overplotting -- 8.2 R: ggplot -- 8.2.1 Categorical Scatterplot -- 8.2.2 Violin Plot and Scatterplot with Jitter -- 8.2.3 Sina Plot -- 8.2.4 Beeswarm Plot.; 8.2.5 Comparison Between Jittering, Sina plot, and Beeswarm plot -- 8.3 Python: Seaborn -- 8.3.1 Strip Plot and Swarm Plot -- 8.3.2 Sina Plot -- Chapter 9 Half‐Violin Plots -- 9.1 R: ggplot -- 9.1.1 Custom Function -- 9.1.2 Raincloud Plot -- 9.2 Python: Seaborn -- Chapter 10 Ridgeline Plots -- 10.1 History of the Ridgeline -- 10.2 R: ggplot -- Chapter 11 Heatmaps -- 11.1 R: ggplot -- 11.2 Python: Seaborn -- Chapter 12 Marginals and Plots Alignment -- 12.1 R: ggplot -- 12.1.1 Marginal -- 12.1.2 Plots Alignment -- 12.1.3 Rug Plot -- 12.2 Python: Seaborn -- 12.2.1 Subplots -- 12.2.2 Marginals: Joint Plot -- 12.2.3 Marginals: Joint Grid -- Chapter 13 Correlation Graphics and Cluster Maps -- 13.1 R: ggplot -- 13.1.1 Cluster Map -- 13.2 Python: Seaborn -- 13.2.1 Cluster Map -- 13.3 R: ggplot -- 13.3.1 Correlation Matrix -- 13.4 Python: Seaborn -- 13.4.1 Correlation Matrix -- 13.4.2 Diagonal Correlation Heatmap -- 13.4.3 Scatterplot Heatmap -- Part II Interactive Graphics with Altair -- Chapter 14 Altair Interactive Plots -- 14.1 Scatterplots -- 14.1.1 Static Graphics -- 14.1.1.1 JSON Format: Data Organization -- 14.1.1.2 Plot Alignment and Variable Types -- 14.1.2 Facets -- 14.1.3 Interactive Graphics -- 14.1.3.1 Dynamic Tooltips -- 14.1.3.2 Interactive Legend -- 14.1.3.3 Dynamic Zoom -- 14.1.3.4 Mouse Hovering and Contextual Change of Color -- 14.1.3.5 Drop‐Down Menu and Radio Buttons -- 14.1.3.6 Selection with Brush -- 14.1.3.7 Graphics as Legends -- 14.2 Line Plots -- 14.2.1 Static Graphics -- 14.2.2 Interactive Graphics -- 14.2.2.1 Highlighted Lines with Mouse Hover -- 14.2.2.2 Aligned Tooltips -- 14.3 Bar Plots -- 14.3.1 Static Graphics -- 14.3.1.1 Diverging Bar Plot -- 14.3.1.2 Plots with Double Scale -- 14.3.1.3 Stacked Bar Plots -- 14.3.1.4 Sorted Bars -- 14.3.2 Interactive Graphics -- 14.3.2.1 Synchronized Bar Plots.; 14.3.2.2 Bar Plot with Slider -- 14.4 Bubble Plots -- 14.4.1 Interactive Graphics -- 14.4.1.1 Bubble Plot with Slider -- 14.5 Heatmaps and Histograms -- 14.5.1 Interactive Graphics -- 14.5.1.1 Heatmaps -- 14.5.1.2 Histograms -- Part III Web Dashboards -- Chapter 15 Shiny Dashboards -- 15.1 General Organization -- 15.2 Second Version: Graphics and Style Options -- 15.3 Third Version: Tabs, Widgets, and Advanced Themes -- 15.4 Observe and Reactive -- Chapter 16 Advanced Shiny Dashboards -- 16.1 First Version: Sidebar, Widgets, Customized Themes, and Reactive/Observe -- 16.1.1 Button Widget: Observe Context -- 16.1.2 Button Widget: Mode of Operation -- 16.1.3 HTML Data Table -- 16.2 Second Version: Tabs, Shinydashboard, and Web Scraping -- 16.2.1 Shiny Dashboard -- 16.2.2 Web Scraping of HTML Tables -- 16.2.3 Shiny Dashboards and Altair Graphics Integration -- 16.2.4 Altair and Reticulate: Installation and Configuration -- 16.2.5 Simple Dashboard for Testing Shiny‐Altair Integration -- 16.3 Third Version: Altair Graphics -- 16.3.1 Cleveland Plot and Other Graphics -- Chapter 17 Plotly Graphics -- 17.1 Plotly Graphics -- 17.1.1 Scatterplot -- 17.1.2 Line Plot -- 17.1.3 Marginals -- 17.1.4 Facets -- Chapter 18 Dash Dashboards -- 18.1 Preliminary Operations: Import and Data Wrangling -- 18.1.1 Import of Modules and Submodules -- 18.1.2 Data Import and Data‐Wrangling Operations -- 18.2 First Dash Dashboard: Base Elements and Layout Organization -- 18.2.1 Plotly Graphic -- 18.2.2 Themes and Widgets -- 18.2.3 ; Reactive Events and Callbacks -- 18.2.4 Data Table -- 18.2.5 Color Palette Selector and Data Table Layout Organization -- 18.3 Second Dash Dashboard: Sidebar, Widgets, Themes, and Style Options -- 18.3.1 Sidebar, Multiple Selection, and Checkbox -- 18.3.2 Dark Themes -- 18.3.3 Radio Buttons -- 18.3.4 Bar Plot -- 18.3.5 Container.; 18.4 Third Dash Dashboard: Tabs and Web Scraping of HTML Tables -- 18.4.1 Multi‐page Organization: Tabs -- 18.4.2 Web Scraping of HTML Tables -- 18.4.3 Second Tab's Layout -- 18.4.4 Second Tab's Reactive Events -- 18.5 Fourth Dash Dashboard: Light Theme, Custom CSS Style Sheet, and Interactive Altair Graphics -- 18.5.1 Light Theme and External CSS Style Sheet -- 18.5.2 Altair Graphics -- Part IV Spatial Data and Geographic Maps -- Chapter 19 Geographic Maps with R -- 19.1 Spatial Data -- 19.2 Choropleth Maps -- 19.2.1 Eurostat - GISCO: giscoR -- 19.3 Multiple and Annotated Maps -- 19.3.1 From ggplot to Plotly Graphics -- 19.4 Spatial Data (sp) and Simple Features (sf) -- 19.4.1 Natural Earth -- 19.4.2 Format sp and sf: Centroid and Polygons -- 19.4.3 Differences Between Format sp and Format sf -- 19.5 Overlaid Graphical Layers -- 19.6 Shape Files and GeoJSON Datasets -- 19.7 Venice: Open Data Cartography and Other Maps -- 19.7.1 Tiled Web Maps -- 19.7.1.1 Package ggmap -- 19.7.1.2 Package Leaflet -- 19.7.2 Tiled Web Maps and Layers of sf Objects -- 19.7.2.1 Tiled Web Maps with ggmap -- 19.7.2.2 Tiled Web Map with Leaflet -- 19.7.3 Maps with Markers and Annotations -- 19.8 Thematic Maps with tmap -- 19.8.1 Static and Interactive Visualizations -- 19.8.2 Cartographic Layers: Rome's Archaeological Sites -- 19.9 Rome's Accommodations: Intersecting Geometries with Simple Features and tmap -- 19.9.1 Centroids and Active Geometry -- 19.9.2 Quantiles and Custom Legend -- 19.9.3 Variants with Points and Popups -- Chapter 20 Geographic Maps with Python -- 20.1 New York City: Plotly -- 20.1.1 Choropleth Maps: plotly.express -- 20.1.1.1 Dynamic Tooltips -- 20.1.1.2 Mapbox -- 20.1.2 Choropleth Maps: plotly.graph&amp -- uscore -- objects (plotly go) -- 20.1.3 GeoJSON Polygon, Multipolygon, and Missing id Element -- 20.2 Overlaid Layers.; 20.3 Geopandas: Base Map, Data Frame, and Overlaid Layers -- 20.3.1 Extended Dynamic Tooltips -- 20.3.2 Overlaid Layers: Dog Breeds, Dog Runs, and Parks Drinking Fountains -- 20.4 Folium -- 20.4.1 Base Maps, Markers, and Circles -- 20.4.2 Advanced Tooltips and Popups -- 20.4.3 Overlaid Layers and GeoJSON Datasets -- 20.4.4 Choropleth Maps -- 20.4.5 Geopandas -- 20.4.6 Folium Heatmap -- 20.5 Altair: Choropleth Map -- 20.5.1 GeoJSON Maps -- 20.5.2 Geopandas: NYC Subway Stations and Demographic Data -- Index -- EULA.
• Sommario/riassunto: Communicate the data that is powering our changing world with this essential text The advent of machine learning and neural networks in recent years, along with other technologies under the broader umbrella of 'artificial intelligence,' has produced an explosion in Data Science research and applications. Data Visualization, which combines the technical knowledge of how to work with data and the visual and communication skills required to present it, is an integral part of this subject. The expansion of Data Science is already leading to greater demand for new approaches to Data Visualization, a process that promises only to grow. Data Visualization in R and Python offers a thorough overview of the key dimensions of this subject. Beginning with the fundamentals of data visualization with Python and R, two key environments for data science, the book proceeds to lay out a range of tools for data visualization and their applications in web dashboards, data science environments, graphics, maps, and more. With an eye towards remarkable recent progress in open-source systems and tools, ; this book offers a cutting-edge introduction to this rapidly growing area of research and technological development. Data Visualization in R and Python readers will also find: Coverage suitable for anyone with a foundational knowledge of R and Python Detailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and others Case studies accompanying each chapter, with full explanations for data operations and logic for each, based on Open Data from many different sources and of different formats Data Visualization in R and Python is ideal for any student or professional looking to understand the working principles of this key field.