Bokeh vs plotly 2019

Bokeh has been around since Bokeh : Anaconda. Dash : Plotly. Bokeh: BSD 3-Clause permissive license. Dash: MIT permissive license. All these licenses are permissive as well. Bokeh: D3 frontendTornado backend. Bokeh : supported natively with interactions. Dash : not supported, but Plotly is. Bokeh: Mostly some graph interactions must be written in javascript. Dash : Yes for the core framework. You can also write custom React components or find some on the internet. These examples show how a selection component can update the graphs.

They also show how selecting data on a graph updated the other components. Both dashboards also look very similar. Another point is interaction consistency. See what happens in the Bokeh example when you first select a category on top, then select data on the scatter plot and then unselect a category. Data in the Bokeh graphs becomes inconsistent. If you come up with an elegant solution to this issue, please let me know. Plotly has always been incredibly intuitive.

Incredibly easy to get live updates and great visualisation. Bokeh was just frustrating when I tried it. The examples are written in Python3. The dataset will be loaded in the following way throughout the examples:. It makes the code simpler here. When giving lines of code for each example, those previous lines will be excluded. Lines of code for the example: Version of Bokeh in the example: 0. I found it hard to use the Bokeh data sources because I wanted and failed to link them to my pandas dataframes.

Configuring graphs to look like I wanted was a hassle, and it took me a lot of time to get around how interactions work. Bokeh has a wider range of interactions than Dash. For example, you can pan multiple graphs at the same time, which is not possible in Dash right now.

Version of Dash in the example: 0. The only difficulty I had was how to use the dcc. Graph object with the regular plotly library. When that was clear for me, development was a breeze.It is said that a picture is equal to words. This article will focus on data visualization with Python and will introduce the most popular data visualization libraries, textbooks, and courses available.

Data Visualization is a very important and often overlooked part of the process of asking the right question, getting the required data, exploring, model and finally communication the answer by setting it for production or showing insights to other people. It is widely used in the Exploratory Data Analysis to getting to know the data, its distribution, and main descriptive statistics. Recently, a black hole was imaged for the first time in history by the Event Horizon telescope made of telescopes all over the world.

Half of ton of hard drives had been used to store the data. It was so big, it had to be flown physically to one place even in our modern time of internet and fast computer networks.

Python and several of the libraries have been used to make this incredible feat possible. For more technical details you could see in the academic paper. Having a better understanding of the data, no matter the source will lead to creating more accurate models. Here is a list of some libraries you can use start with. Matplotlib is a low-level library for creating two-dimensional diagrams and graphs.

It is the oldest Python visualization library and the most developed with the most commits and contributors as of With its help, you can build diverse charts, from histograms and scatterplots to non-Cartesian coordinates graphs. Moreover, many popular plotting libraries are designed to work in conjunction with matplotlib. There have been style changes in colors, sizes, fonts, legends, etc.

bokeh vs plotly 2019

As an example of appearance improvements are an automatic alignment of axes legends and among significant colors improvements is a new colorblind-friendly color cycle. Source: ActiveWizards. Most visualization courses teach it for example:. Seaborn is a library for making statistical graphics in Python. It contains more suitable default settings for processing charts. Also, there is a rich gallery of visualizations including some complex types like time series, jointplots, and violin diagrams.

The seaborn updates mostly cover bug fixes. However, there were improvements in compatibility between FacetGrid or PairGrid and enhanced interactive matplotlib backends, adding parameters and options to visualizations.

The quality of the charts is typically higher then matplotlib and are easier to build without much customization.

It is sometimes covered in some courses, but there are fewer courses on it compared to matplotlib. For example:. While Matplotlib and Seaborn are static, the Bokeh library creates interactive and scalable visualizations in a browser using JavaScript widgets. No need to learn JavaScript — You can read more in here. The library provides a versatile collection of graphs, styling possibilities, interaction abilities in the form of linking plots, adding widgets, and defining callbacks, and many more useful features.

Bokeh can boast with improved interactive abilities, like a rotation of categorical tick labels, as well as small zoom tool and customized tooltip fields enhancements. Source: bokeh. Plotly is a newer interactive library that allows you to build sophisticated graphics easily. The package is adapted to work in interactive web applications.

Matplotlib vs. Seaborn vs. Plotly

Among its remarkable visualizations are contour graphics, ternary plots, and 3D charts. Dash is a productive Python framework for building web applications.When analyzed and utilized properly, data helps to improve processes.

That said, with modern data collection processes leading to the creation of rather large datasets, it can be difficult to effectively analyze data in a manner that provides the context needed to improve such processes. Enter data visualization.

Data visualization is the graphic representation of data for the purpose of contextualizing said data. Data visualization allows us to see trends in datasets, and gives us the ability to identify the outlying data points that often lead to useful conclusions.

These are critical steps in processing data effectively. So what technologies can we use to develop effective graphic representations of large datasets? As of today, there are several popular Python libraries for developing interactive, web-based data visualization applications. If you want to follow along with the examples, make sure you have a recent version of Python installed along with Dash, Plotly, Bokeh and Pandas.

To get started quickly, you can either:.

Charting the waters: between Bokeh and D3

All the code in this post, along with the temperature dataset I used can be found in my Github repository here. The two most popular frameworks for Python, Django and Flask, take incredibly different approaches to web development. The benefits of this framework are far-reaching.

The library itself was developed utilizing Plotly. This results in one particular advantage of working with Dash: you can write pure Python and allow the framework to handle the rest.

bokeh vs plotly 2019

This is ideal for developers who have more experience with Python than other languages, and enables these Python developers to get their data visualization solutions up and running in a relatively quick manner. Dash does offer the flexibility for engineers to develop extensions for use with their Dash web applications. So, in the instance where a Python developer using Dash is also familiar with pure JavaScript and the React. The application will display high and low temperatures by year on a scatter plot, with the x-axis representing the year the temperature was recorded and the y-axis representing the temperatures in degrees Fahrenheit.

I will also show how to add a range slider, allowing the data analyst using the app to expand or limit the years displayed by the scatter plot.Shiny is by leaps and bounds the most popular web application framework for R.

It provides the convenient ability to write fully dynamic web applications using only R code. Dash is a fairly new Python web application framework with the same approach. Although Dash is often thought of as Python's Shiny, there are some important differences the should be highlighted before you run off and re-write all your Shiny apps with Dash. In this post I'm going to start by comparing some Shiny code to Dash code for an equivalent app. I'll then move on to talking about a couple of the unseen differences between the two: the ability to share data across callbacks, and ease of deployment.

We'll start with a little setup. We'll use the mtcars data from R and use linear regression to predict a car's miles per gallon from a number of cylindars cyldisplacement dispquarter mile time qsecand if the car is manual or automatic am. I chose these because it gives us a nice preview of the different types of selectors on the UI side: sliders, radio buttons, and boolean value selection.

Data Visualization with Bokeh and Django

First let's dive into the Shiny app. For those familiar with Shiny, this will be very straight forward--it reads like many of the examples in shiny man pages and tutorials. I think something that really stands out well here is the simplicity--this app comes in at just 35 lines of code--and that includes comments! Inputs and outputs are well defined and the flow of the app is easy to understand. At no point have we had to mess with css, div tags, or really think about the UI.

Despite that, we get a UI that looks really nice. I am especially happy with how easy it is to get good looking sliders with almost no configuration--something that isn't so simple in Dash. For those unfamiliar with Dash, it has a similar conceptual layout as Shiny: The app is broken up into a section for the UI and a section for server side processing.

We also have a concept of inputs and outputs, and like shiny, outputs can be fed into other server side functions for further processing. Let's take a look at the code. It's pretty straight forward.I started with matplotlibthen proceeded to make more complex plots with seabornand interactive models with Bokeh and Plotly.

Each one of these tools work in a different way and are capable of doing different things. Here is a summary of my thoughts and experience with them. I will be presenting some visualization examples for each tool. All of them are based in the Iris Datasetwhich can be imported with seaborn with:. Matplotlib is the basis for static plotting in Python.

Many other visualization tools are built on top of it, such as seaborn and Pandas DataFrames plot method. It is versatile meaning it is able to plot anything, but non-basic plots can be very verbose and complex to implement.

On the other hand, basic plots such as histograms and scatter plots are very easy to do. If your goal is to make simple and quick basic plots, there is no big drawback. However, for more complex such as pair plots and heat maps, it is interesting to use some higher-level tools.

We can quickly build a standard histogram with the following lines:. However, we need to overlap plots for a simple customization like coloring according the species:.

bokeh vs plotly 2019

Considering such difficulty for a basic task, I recommend using seaborn for plotting anything multi-dimensional. Seaborn is my go-to tool for static plotting. It integrates very well with Pandas DataFrames, making it possible to assign column names to the axis, which makes the code clearer. The plots are naturally prettier and easy to customize with color palettes. There are many built-in complex plots like cluster maps, which are very convenient for analyzing data quickly and effectively.

Making complex plots with low effort is accomplished by some features like automatic labels for the axes and grouping with specialized support for categorical variables. Even complex tasks like multi-plotting are abstracted to a high-level grid structure. Documentation is my favorite thing about seaborn. Everything is minimalist and very well organized. All parameters are well explained in a simple language and didactic examples are provided along with the documentation page, demonstrating how some parameters can affect the visualization.

So, everything you need is in one place, and in a non-overwhelming way. Looking at the examples, you can find the most interesting plots to analyze a specific dataset. For example, I thought the strip plot would give me good insights for the Iris Dataset. In fact, the return type of stripplot is a matplotib axes, meaning we can use all its methods if we want to add or change something from what seaborn generated.

In the strip plot example, we added a legend with plt module. Bokeh is a web-focused tool for creating interactive plots. It supports streaming datasets and integrates with Pandas by using the ColumnDataSource class. There are built-in tools that can be included on a widget box attached to the plot and used to explore the data in an interactive way, such as zooming in, selecting and overlaying a crosshair.

Plotting with this tool is different because it is built around glyphs. The higher-level interface bokeh. To plot a histogramfor example, the edges of each bin must be calculated to compose two lists, which are passed to plotting method quad that displays Quad glyphs. The obtained plot is pretty and interactive, but a lot of manual work is required.

Bokeh server is a Flask Blueprint for building interactive web applications.This post is the first in a three-part series on the state of Python data visualization tools and the trends that emerged from SciPy At a special session of SciPy in Austin, representatives of a wide range of open-source Python visualization tools shared their visions for the future of data visualization in Python.

We heard updates on MatplotlibPlotlyVisPyand many more. This first post surveys the packages currently available and shows how they are linked, and subsequent posts will discuss how these tools have been evolving in recent years, and how they will go forward from here. Here, you can see several main groups of libraries, each with a different origin, history, and focus. These tools VisPyglumpyGRMayaviParaViewVTKand yt primarily build on the OpenGL graphics standard, delivering graphics-intensive visualizations of physical processes in three or four dimensions 3D over timefor regular or irregularly gridded data.

InfoVis libraries use the two dimensions of the printed page or computer screen to make abstract spaces interpretable, typically with axes and labels. The InfoVis libraries can be further broken down into numerous subgroups:. One of the oldest and by far the most popular of the InfoVis libraries, released inwith a very extensive range of 2D plot types and output formats.

Matplotlib includes some 3D support, but much more limited than the SciVis libraries provide. Once HTML5 allowed rich interactivity in browsers, many libraries arose to provide interactive 2D plots for web pages and in Jupyter notebooks, either using custom JS BokehToyplot or primarily wrapping existing JS libraries like D3 Plotlybqplot.

Bokeh vs Dash — Which is the Best Dashboard Framework for Python?

Wrapping existing JS makes it easy to add new plots created for the large JS market as for Plotlywhile using custom JS allows defining lower level JS primitives that can be combined into completely new plot types from within Python as for Bokeh. As JavaScript libraries like D3 have matured, their functionality has been captured in declarative JSON specifications VegaVega-Litemaking it simple to generate JavaScript plots from any language, now including Python via Altair and previously via vincent.

Having the full plot specification available as portable JSON allows integration across many types of tools. None of these newer web-based 3D approaches approach the breadth and depth of the desktop SciVis 3D libraries, but they do allow full integration with Jupyter notebooks and easy sharing and remote usage via the web.

So even though WebGL tools have some applications in common with the SciVis tools, they are probably more closely tied with the other InfoVis tools. The above breakdown by history and technology helps explain how we got to the current profusion of Python viz packages, but it also helps explain why there are such major differences in user-level functionality between the various packages.

Specifically, there are major differences in the supported plot types, data sizes, user interfaces, and API types that make the choice of library not just a matter of personal preference or convenience, and so they are very important to understand:. The most basic plot types are shared between multiple libraries, but others are only available in certain libraries.

As a rough guide:. Statistical plots scatter plots, lines, areas, bars, histograms : Covered well by nearly all InfoVis libraries, but are the main focus for Seaborn, bqplot, Altair, ggplot2, plotnine. The architecture and underlying technology for each library determine the data sizes supported, and thus whether the library is appropriate for large images, movies, multidimensional arrays, long time series, meshes, or other sizeable datasets :.

SciVis : Can generally handle very large gridded datasets, gigabytes or larger, using compiled data libraries and native GUI apps. Matplotlib-based : Can typically handle hundreds of thousands of points with reasonable performance, or more in some special cases e.

JavaScript : ipywidgets, Bokeh, and Plotly all use JSON but augment it with additional binary-data transport mechanisms so that they can handle hundreds of thousands to millions of data points. Server-side rendering : External InfoVis server-side rendering from Datashader or Vaex allows billions, trillions, or more data points in web browsers, by converting arbitrarily large distributed or out-of-core datasets into fixed-sized images to embed in the client browser.

Because of the wide range in data size and thus to some extent data type supported by these types of libraries, users needing to work with large sizes will need to choose appropriate libraries at the outset. The various libraries differ dramatically in the ways that plots can be used. Native GUI app : The SciVis libraries plus Matplotlib and Vaex can create OS-specific GUI windows, which provide high performance, support for large data sets, and integration with other desktop applications, but are tied to a specific OS and usually need to run locally rather than over the web.

In some cases, JavaScript-based tools can also be embedded in native apps by embedding a web browser. Jupyter notebooks : Most InfoVis libraries now support interactive use in Jupyter notebooks, with JavaScript-based plots backed by Python.

bokeh vs plotly 2019

The ipywidgets-based projects provide tighter integration with Jupyter, while some other approaches give only limited interactivity in Jupyter e. HoloViews when used with Matplotlib rather than Bokeh. Standalone web-based dashboards and apps : Plotly graphs can be used in separate deployable apps with Dashand Bokeh, HoloViews, and GeoViews can be deployed using Bokeh Server.Plotly allows you to make beautiful, interactive, exportable figures in just a few lines of code.

However, without a map, the the path up Mt. Here are the stumbling blocks that hold back the adventurous as they are trying to make their way:. The view is worth, it.

Trust me. The company behind Plotly is also named Plotly. It has open-sourced a slew of interactive visualization products. It makes money by offering enhanced functionality for many products. It also offers private hosting for a fee. The company is based in Montreal, with an office in Boston. Note that not all languages have all example docs available. Install the vanilla plotly. Import the module and configure it to work offline:. Below is a lengthy Gist for the plotly.

Plotly objects consist of one or more data components and a layout component. Both have subcomponents. Most, but not all, of the formatting is controlled in the layout. The main reason to avoid vanilla plotly. Specifying lines and lines of code is slow and error prone. We definitely hit our second hurdle on our adoption path.


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