bokeh altair Hamid a 4 postes sur son profil. Altair is based on the powerful vega-lite Bokeh is a highly flexible and effective data visualization library in Python that can be used to create rich interactive visualizations. I was wondering, whether reports can be generated with Bokeh/altair doing the plotting as opposed to Matplotlib? Both Altair and Bokeh render web-based graphics. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications. Parameters. 6] to show small figures for saving space. Altair. org) Movie Explorer (runs in mybinder. After watching a great webinar about plotting with different python libraries, I wanted to see what it was like to make a stress strain curve using four different modules: pandas, matplotlib, altair and bokeh (with holoviews). That means you describe the properties of the data and the object can plot itself from these properties. Bokeh. , tooltips and zooming), Altair benefits -- seemingly for free! import altair as alt from vega_datasets import data from math import pi import pandas as pd from bokeh. It is also known by the alias array. Altair is a simplified Python library for creating statistical quality visualizations. All of the Jupyter notebooks to create these charts are stored in a public github repo Python-Viz-Compared. Each library has its own unique features and quirks, some related to each other, while some are based on completely different technologies and ideas. Altair is a declarative plotting library for Python, based on Vega. Like Bokeh, Altair brings a clear, comprehensible grammar and scheme to designing plots. plotting. register_converters. Figure 3: Bokeh linked panning demo Figure 4: Altair sample visualisation Figure 5: Altair Streamgraph. Bokeh gives high-performing intelligence, with the concise construction of novel graphics over very large or even streaming datasets, in a quick, easy way and elegant manner. py Flask app. Altair is a declarative statistical visualization library and it is based on Vega and Vega-Lite. . Set to False to The feature that sets Bokeh apart is its ability to easily implement interactivity in your visualization. Here is an example using Altair to quickly visualize and display a dataset with the native Vega-Lite renderer in the JupyterLab: Bokeh: Bokeh is an Excellent Python data visualization library which targets latest browsers presentation. Oversized lens. Over the span of 11 chapters (270+ pages) this book will cover 9 Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy. Add a "boxplot" layer to a Bokeh figure. It provides some interactivity and the code is quite readable and short. Online documentation is at https://six. I couldn’t stop thinking about the power these two libraries provide to data scientists using Python across the globe. The key is "pandas_plotting_backends". In [1]: import panel as pn import pandas as pd import altair as alt from bokeh Finally, I am not saying that you should avoid the other good options like ggplot (aka ggpy), bokeh, plotly or altair. palettes import Category20c, Category20 from bokeh. There are many methods that you can use to get that understanding: Look at evaluation metrics (also you should know […] COVID-19 in Senegal Live application less than 1 minute read On this page. In Altair, users start with a “Chart” object, then build on that with adding the chart type (such as “bar”), 👉 Altair. I recommend it to most of my data science students. With Altair, you can spend more time understanding your data and its meaning. To access them yourself, install vega_datasets. Ask Question After also exploring plotly and bokeh, Altair gave me the closest solution (below). Bokeh is an interactive data visualization library for Python—and other languages—that targets modern web browsers for presentation. array, which only handles one-dimensional arrays and offers less Everyone agrees that altair needs to talk to mpl / bokeh / holoviews / bqplot, the limiting factor is resources to make it happen. Altair plots Plotly plots Bokeh plots All data types NBA Scoring jupyter-widgets-gallery (runs in binder. Introduction. The arguments to this function closely follow the ones for Bokeh’s show function. which produces an upright, correctly orientated image. Altair Bokeh is a modern plotting library that is best used for creating interactive two-dimensional visualizations that are intended to be displayed in Jupyter Notebooks and/or HTML web sites. Libraries implementing the plotting backend should use entry points to make their backend discoverable to pandas. Next, I'll look at Altair, whose declarative API means it can make really complex plots without causing brain ache. Each library has its own unique features and quirks, some related to each other, while some are based on completely different technologies and ideas. Date Sun 21 October 2018 Tags python / matplotlib / engineering / jupyter / notebook / bokeh / altair / pandas After watching a great webinar about plotting with different python libraries, I wanted to see what it was like to make a stress strain curve using four different modules: pandas, matplotlib, altair and bokeh (with holoviews). Streamlit vs. As with Bokeh, Altair outputs its plots as HTML files. . io import output_file, show Since Jupyter-flex dashboards have a web frontend, either static . Bokeh is a charting library for Python. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Pygal. Today Python boasts of a large number of powerful visualisation tools like Plotly, Bokeh, Altair to name a few. You can plot directly from Python and so this can reduce the need to return a lot of data to Excel and make your sheets smaller and simpler. from bokeh. Jupyter Notebook has support for many kinds of interactive outputs, including the ipywidgets ecosystem as well as many interactive visualization libraries. 2. While there are several options for plotting data in Python—including Matplotlib, seaborn, Bokeh, and plotnine, to name a few—the fact that there are so many libraries is telling. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh is a library that allows you to generate interactive graphics. Voila Altair is developed by Jake Vanderplas and Brian Granger in close collaboration with the UW Interactive Data Lab. WebGL Altair definition is - the brightest star in the constellation Aquila. In general, you don't want to recreate views, you want to recreate as few Bokeh models as possible. The quickest path to that is an If you are generating a lot of graphs or are working with very large datasets but wish to retain the interactivity, use Bokeh or Altair instead. Bokeh even goes as far as describing itself as an interactive visualization library: Bokeh is an interactive visualization library that targets modern web browsers for presentation. Dash components & demos to create Altair, Matplotlib, Highcharts , and Bokeh graphs within Dash apps. Bokeh is an interactive visualization library for modern web browsers. Altair enables you to build a wide range of statistical visualizations quickly with a powerful and concise visualization grammar. Consultez le profil complet sur LinkedIn et découvrez les relations de Hamid, ainsi que des emplois dans des entreprises similaires. html. Even among a variety of options, Seaborn is one of the best. plotting First, we import the Pandas library and the basic elements from Bokeh (i. Brian Granger (Jupyter/Altair) (based on Vega/Vegaliite). Altair. array is not the same as the Standard Python Library class array. These steps are commonly used to generate a chart using Altair. Once created, reports can be published on the web, dynamically generated in the cloud, or embedded into your own application, where data can be explored, and visualizations can be used interactively. Bokeh provides the model-view-controller framework on which Panel is built, along with many of the core components such as the widgets and layout engine Param provides a framework for reactive parameters which are used to define all Panel components. With Altair, you can spend more time understanding your data and its meaning. Bokeh has support with different languages (Python, R, Lua, and neptunecontrib. Interactive data visualizations¶. “I chose Neptune over WandB because it is more lightweight and I’m more comfortable working with it. gl, Bokeh, Plotly, HvPlot/ HoloViews, … Panel layouts, widgets, extensions and apps; You can read the motivation for why I created the Awesome Panel Designer here. I used to visualize most of my work in matplotlib and Seaborn (after trying Bokeh, Plotly, plotnine, among others), but when I discovered Altair I slowly switched to do most of my visualization to Altair! I still use other libraries (specially Seaborn), but I just love Altair's features. Bokeh? Altair?? - turned into a fun adventure of really getting to know the plethora of libraries available in Python, and what each one brings to the table. Bokeh. Figure) – A Bokeh Matplotlib simulates raindrops on a surface by animating the scale and opacity of 50 scatter points. Dash, while Bokeh, HoloViews, and GeoViews can be deployed using Bokeh Server. The website content uses the BSD License and is covered by the Bokeh Code of Conduct. Document your charts!!! When plotting make sure to have explanatory text above or below the chart — explain to the reader what they are looking at, and walk them through the insights and conclusions Altair is a declarative statistical visualization library, built on top of Vega and Vega-Lite. Here’s a clone link that gives you an IFrame component you can use as a dependency: Integrations with Matplotlib, Plotly, Bokeh, AltairDocumentation: https://docs. Using Python’s plotting packages is preferable to using Excel’s own charts in some situations. This means it's a well-thought-through API that scales well for complex plots, saving you from getting lost in nested-for-loop hell. Shiny integrates well with plotting libraries in the R ecosystem, such as ggplot2, while Streamlit integrates with Python plotting libraries such as Bokeh or Altair. log_chart (name, chart, experiment=None) [source] ¶ Logs charts from matplotlib, plotly, bokeh, and altair to neptune. The lens actually has a larger diameter than 60mm, however, to improve outer edge performance, it has been limited to 60mm. Matplotlib, Vega/ Altair, ECharts, Deck. pydata. It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations. io is used to establish where the output plot is intended to be displayed. Let’s see how you can add a Bokeh plot to your hello. We can export them to an HTML document that we can share with anyone who has a web browser. They can use other plotting libraries like Bokeh, Altair, etc. This is the approach that spark-notebook takes, for example. These libraries are able to achieve state of the art animations and interactiveness. api. figure (bokeh. api. org) bokeh. Here's a guide to using Github+Kyso [1] to publish your type of article to the web, it should be a very similar workflow to github pages, and you can use any of the popular python visualization libraries - we support plotly, bokeh, vega, altair, matplotlib etc. log_chart (name, chart, experiment=None) [source] ¶ Logs charts from matplotlib, plotly, bokeh, and altair to neptune. Altair: apply selection and ~selection within LayerChart. models gives the user a way to turn Python dictionaries or Pandas DataFrames into data that Bokeh can display quickly. It provides some interactivity and the code is quite readable and short. 6. More specifically, over the span of 11 chapters this book will cover 9 Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy. Altair. figure. g. Python has a variety of data visualization packages, including Matplotlib, Matplotlib’s Pyplot, Bokeh, Altair, and many others. Almost 10 PieCharts 10 Python Libraries Here is a follow-up to our “10 Heatmaps 10 Libraries” post. In Bokeh, it is possible to pass lists of values directly into plotting functions. The Bokeh and Altair examples don't work for me but I suspect it's something to do with my setup (versions). Bokeh can create reasonably complex graphics with fewer lines of code and higher resolution. bokeh. html files or a running webserver, in general any library that outputs a web based plot will look better, for example: Altair, plotly, Bokeh and bqplot. The possibilities for “custom” logging are practical and the tool is really stable. e. Note that numpy. highcharts seaborn vega vega-lite bokeh matplotlib altair holoviews plotly-dash Updated Dec 30, 2020 Altair is declarative visualization library for python. 5. This gives you dynamic plots in the browser. org if you want to get involved. Altair Documentation. Bokeh is a Python library for making interactive “D3” style plots using a imperative style like Matplotlib (versus a declarative style like Altair). log_chart (name, chart, experiment=None) [source] ¶ Logs charts from matplotlib, plotly, bokeh, and altair to neptune. neptune. Create a Chart object passing dataframe to it. This means a well-thought-through API that scales well for complex plots, saving you from getting lost in nested-for-loop hell. You will need an IFrame component to display them in. But, if you are looking to build an enterprise-level app, if performance is of paramount importance, if you are looking to style the app to your liking (e. html. Most of the other InfoVis libraries can be deployed as dashboards using the new Panel library, including Matplotlib, Altair, Plotly, Datashader, hvPlot, Seaborn, plotnine, and yt, with varying levels of interactivity. There are few if any spotting scopes which perform like an triplet ED astronomy refractor in terms of colour correction and sharpness at high powers. (The copyright and license notice must be retained. For those of you who don’t remember, the goal is to create the same chart in 10 different python visualization libraries and compare the effort involved. api. Now the reason we can get to 95,000 points there is ’cause bokeh along with Plotly will embed the data in a binary form. One of the defining characteristics of statistical visualization is that it begins with tidy Dataframes. Otherwise use Streamlit (or Dash – see above). This means that you can define plots and supply data for them from within applications that link these libraries in. Register custom converters with matplotlib. Polyaxon supports two methods for logging matplotlib figures: Logging figures as images; Parsing the figures to interactive charts; Matplotlib static figures Altair Here also the dataframe is used but as the data is in wide format it is converted into long format and then plotted. Bokeh is one of the four most popular plotting libraries, and this series is looking into what makes each of them special. The backend module can then use other visualization tools (Bokeh, Altair,…) to generate the plots. 1 Matplotlib First, figsize of figure is set to [2, 1. fill_color: color to use to fill the glyph with - a hex code (with no alpha) or any of the 147 named CSS colors, e. Bokeh is a fiscally sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open-source scientific computing community. Backends can be implemented as third-party libraries implementing the pandas plotting API. Declarative graphics APIs: The Grammar of Graphics-inspired libraries like ggplot, plotnine, Altair, and (to some extent) Bokeh provide a natural way to compose graphical primitives like axes and glyphs to create a full plot. htmlFor examples check https://ui . At this point, I don’t have a clear recommendation on which one is neptunecontrib. Altair Astro pioneered this approach and our customers always get the best quality. figure, output_file, show, and ColumnDataSource). org) bqplot plots (runs in mybinder. Recently, I was going through a video from SciPy 2015 conference, “Building Python Data Apps with Blaze and Bokeh“, recently held at Austin, Texas, USA. Frankly, there’s almost too many Python visualization packages to keep track of. ) NumPyâ s array class is called ndarray. Once you understand the basic visualization stack, you can explore the other options and make informed choices based on your needs. Interactive Data Visualization with Python, 2nd Edition by Abha Belorkar, Anshu Kumar, Sharath Chandra Guntuku, Shubhangi Hora. See Altair's Documentation Site, as well as Altair's Tutorial Notebooks. I am assuming u meant the chart widgets in shiny? It has been ages since I last touch R, but I think it is nvD3? Altair. Example. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. bokeh_chart (figure, use_container_width = False) ¶ Display an interactive Bokeh chart. Whilst it requires more work than other methods to plot data its customizability makes it ideal for more advanced users who are looking to create thier own visualizations. To plot a bokeh figure in Excel you first create the figure in exactly the same way you would in any Python script using bokeh, and then use PyXLL’s plot function to show it in the Excel workbook. And now we have this here, so we went from pandas to data visualization, 95,000 points. I am not sure about ur comparison with shiny as shiny is just a web app framework for R. Another possibility is setting up the Bokeh server and publishing plots and data to it. Starting this blog helped me immensely in learning some of the inner workings of Python visualization, and it pushed me to try different libraries, techniques and methods. Altair seems well-suited to addressing Python's ggplot envy, and its tie-in with JavaScript's Vega-Lite grammar means that as the latter develops new functionality (e. It also includes some example vega datasets. chart. Python Bytes podcast delivers headlines directly to your earbuds. That means you describe the properties of the data and the object can plot itself from these properties. 0. The descriptive method means that only the starting and ending points are provided for building the visualizations. streamlit. Many draw upon sample datasets compiled by the Vega project. In the example below, the data, x_values and y_values, are passed directly to the circle plotting method (see Plotting with Basic Glyphs for more examples). org) Wealth of Nations (runs in mybinder. Go to discourse. Bokeh is an interactive visualization library for modern web browsers. In this talk, I will present Altair, one of its newest additions. Hi all I'm keen to see how you guys share any fancy interactive visualisations with non Python users? I really like the hover / tooltip feature with Plotly Express but couldn't find a way to share those beautiful graphs with managers who don't have Python installed. html. To draw this diagram we used Streamlit’s native bar_chart() method, but it’s important to know that Streamlit supports more complex charting libraries like Altair, Bokeh, Plotly, Matplotlib and more. Plotly, Bokeh, and Altair charts are converted to interactive HTML objects and then uploaded to Neptune as an artifact with path charts/{name}. Altair is based on a declarative plotting language (or "visualization grammar") called Vega. Altair: bokeh: Repository: 6,513 Stars: 14,885 148 Watchers: 472 586 Forks: 3,672 72 days Release Cycle Altair is based on a declarative plotting language (or ‘visualisation grammar’) called Vega. Altair translates simple Python code into the JSON-format Vega-Lite language. You need to install it the following way, if you are using Jupyter Notebook. True. Plotly, Bokeh, and Altair charts are converted to interactive HTML objects and then uploaded to Neptune as an artifact with path charts/{name}. The The phrase “Every model is wrong but some are useful” is especially true in Machine Learning. Common Steps to Generate Charts using Altair ¶ The generation of charts using Altair is a list of steps that are described below. You can find more about Bokeh at https://bokeh. I just think you’ll need a basic understanding of matplotlib + pandas + seaborn to start. I'm also looking at a couple of libraries that stand out for their interesting approach. readthedocs. Some may seem fairly complicated at first glance, but they are built by combining a simple set of declarative building blocks. When the figure is exported to Excel it first has to be converted to an image. This improves the edge performance of the optics, and gives better "Bokeh" with an optimum focal ratio of F6. The imports relevant to our discussion are shown below. Altair is a simple, friendly and consistent expressive and declarative statistical visualization python library based on Vega-Lite. io/. altair; bokeh; pygal; the example. ©2021 Bokeh contributors. chart. Python offers many powerful libraries for visualization (Bokeh, Seabourne, Matplotlib etc). This library is based on Vega-Lite. bokeh. You can log charts generated by Matplotlib directly to Polyaxon. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. As Data becomes more and more important in all spheres of life, interactive data visualization on the web, can go a long way in understanding and processing large amounts of complex data. Altair is a declarative plotting library for Python, based on Vega. We also make two new imports: Spectral5 is a pre-made five color pallette, one of Bokeh’s many pre-made color palettes, and factor_cmap is a helper method for mapping colors to bars in a bar-charts. It is based on the powerful vega-lite javascript library and it allows for - Creating graphs using an intuitive grammar - Client level interaction - Interactions across graphs - Styling and integrating with the broader website without limiting front end developers Stocks altair ¶ Download this notebook from GitHub (right-click to download). I started really simple. . Use Shiny if you prefer doing data analysis in R and have already invested in the R ecosystem. Providing data directly¶. I have written about dash and bokeh in prior articles and I encourage you to review them if you’re interested. Bokeh, Altair and Pygal produce HTML or SVG plots that you can display in an IFrame. For example, pandas registers the default “matplotlib” backend as follows. I tried the author's downloaded code with the same result in case I had some weird syntax problem that I wasn't able to figure out. It was released by 2016 and created by Jake Vanderplas and Brian Granger. Altair Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. plotting. org) Iris clustering (runs in mybinder. a corporate style guide), Dash is the way to go. I tried the author's downloaded code with the same result in case I had some weird syntax problem that I wasn't able to figure out. Altair. GitHub Gist: instantly share code, notes, and snippets. Cons: Bokeh requires more code to create graphs similar to Seaborn, Altair, or Plotly. Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key Features Study and use To use your 80 EDT-R as a spotting scope, simply plug in the Altair Lightwave 45 degree erect image prism diagonal and add an eyepiece. D. Chart¶ neptunecontrib. g. Python boosts a wide range of powerful data visualization libraries ranging from ones primarily designed to create static plots like matplotlib and seaborn to interactive display tools like bokeh, altair, and plotly. More recently, a number of alternate visualization libraries have emerged – Bokeh, Plotly, and Altair, to name a few, all start from scratch to design a more consistent & powerful visualization experience for Python. In this course, you’ll learn how to work with mpl, seaborn, and holoviews, a wrapper on top of bokeh. It is a very useful library when we are interested in looking for things in the graphics and we want to be able to zoom in and move around the graphic. plotting provides functions to create figures and glyphs for a plot/graphic. To make a comparison, I tested six python libraries on these same criteria: matplotlib, seaborn, bokeh, altair, plotnine, and plotly. It is based on the high-level Vega-Lite visualisation grammar that provides JSON syntax for the production of visualisations. Some folks want to focus on high level statistical charting (Altair, Chartify) Some folks want to focus on interactive data exploration (Holoviews) Some folks want to focus on high performance and streaming (Bokeh) Some folks want to focus on high quality static image generation (MPL). vincent, d3po, and mpld3 are no longer maintained and developed. Other packages like Altair and some others, when they embed the data they embed it as JSON, which is much less compact. Bokeh is an interactive visualization library for modern web browsers. In this section, we study how to embed images created by Matplotlib, Bokeh, and Altair libraries. It is also compatible with multiple visualisation packages like Bokeh, Altair or matplotlib. When developing machine learning models you should always understand where it works as expected and where it fails miserably. Altair. Ideally, you would have to just change the indices field of the filter. For the purposes of this tutorial, we’ll start by importing Pandas and creating a simple DataFrame to visualize, with a categorical variable in column a and a numerical variable in column b: This gallery contains a selection of examples of the plots Altair can create. Please try again if you previously found rough edges. matplotlib. Check out the extended guide to Altair here. bokeh. Altair outputs its plots as HTML files. Interactive maps with Bokeh¶ Our ultimate goal today is to learn few concepts how we can produce nice looking interactive maps using Geopandas and Bokeh such as: Accessibility by PT to Helsinki City center Some folks want to monetize their special sauce (Plotly). It has a simple interface that allows for quickly creating great looking figures. Altair is a statistical visualization library in python that uses the descriptive method for plotting the data. from Nanyang Technological University, Singapore. To find out more about my assessments, and to see samples of my code and plots from all these libraries, join me at ODSC East 2021 in my session, “ Going Beyond Matplotlib and Seaborn: A survey of Python Data Python has lagged behind R and ggplot2 in the visualization space. Due to the lack of regularly updated database of COVID-19 cases in Senegal, I decided to build an open database and a web application to display this information. With Altair, you will be able to create meaningful, elegant, and effective visualizations with just a few lines of code and in a very short time. 👉 Bokeh Hi everybody. Data in Altair is built around the Pandas Dataframe. He received his bachelor's degree in computer science from Birla Institute of Technology and Science, Pilani, India and his Ph. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. Pygal focuses on visual appearance. Make a new folder called myapp-0/ When the user searches, the results can Ajax autocomplete here with images. Sharath Chandra Guntuku is a researcher in natural language processing and multimedia computing. Voir le profil de Hamid ITOUDJ sur LinkedIn, le plus grand réseau professionnel mondial. Bokeh is ideally suited for embedding plots in a web app like Flask. Altair is building some unique capabilities in this space, but I think there will still be a need for building quick and useful applications for visualizing data. Datapane reports are flexible and can also contain pages, tabs, drop downs, and more. org. Why Even Try, Man? I recently came upon Brian Granger and Jake VanderPlas's Altair, a promising young visualization library. But there's some missing wiring in Bokeh, so you will have to set the filters field of the view, as below Bokeh offers several alternate language bindings, including one for Scala. Plotly, Bokeh, and Altair charts are converted to interactive HTML objects and then uploaded to Neptune as an artifact with path charts/{name}. Change the plotting backend to a different backend than the current matplotlib one. The Data¶. Altair is based on the declarative statistical visualisation approach available for Python. Bokeh is not supported by only one company, with widely spread developers, NumFocus support, and a completely open model. ai/integrations/visualization_tools. Hi there, Only discovered this project today. Call marker type (mark_point(), mark_bar(), etc) on chart object to select chart type that will be plotted. Each Jupyter notebook will bokeh: Altair: Repository: 14,849 Stars: 6,493 470 Watchers: 148 3,666 Forks: 584 29 days Release Cycle Altair is a declarative statistical visualization library for Python. Donations help pay for cloud hosting costs, travel, and other project needs. g 'green', 'indigo' Your code: Step into Panel with your existing work :) Continue to use the PyData ecosystem that you already know and love: matplotlib, seaborn, ggplot, bokeh, plotly, altair, echarts, holoviews, dask, datashader, and more! brick networkx basemap /cartopy javascript pythreejs bqplot bokeh toyplot plotly ipyvolume cufflinks holoviews datashader d3js mpld3 Altair Vincent OpenGL Glumpy Vispy ipyleaflet Lightning GlueViz YT d3po Vega-Lite Vega MayaVi graphviz GR framework PyQTgraph pygal chaco Vaex graph-tool Visualization for Larger Data . Excited about the impact seen of having a declarative viz When the user searches, the results can Ajax autocomplete here with images. The Bokeh and Altair examples don't work for me but I suspect it's something to do with my setup (versions). Three types of plots: a simple bar, a dot plot (basically a bar plot, but makes Tufte proud with a higher Bokeh; Altair/Vega; Matplotlib. bokeh altair