Brooke  Giles

Brooke Giles


Top 12 Data Visualization Tools You Must Know

Top 12 Data Visualization tools to choose from: Tableau, Google Charts, Zoho Analytics, Microsoft Power BI, Qlik Sense, Plotly, Domo, Infogram, D3.js, Databox, Datawrapper, Highcharts

Data visualization is the process of interpreting or representing data graphically. Generally, it is the graphical or visual representation of data using charts, tables, histograms, and other info-graphics.

Data plays a major role in any type of organization. Data visualization helps us understand certain trends and insights, and make important decisions based on them.

Through visual representation, you can show complex loads of data in a very simplistic manner.

What Are Data Visualization Tools?

Imagine going through a record of one million users and grouping them based on their age range. That would be a very tedious task.

Data visualization tools extract information from such records and present them to you or to anyone who needs the data in a visual way.

In the next section, you'll see some of the best data visualization tools to choose from.

Top Data Visualization Tools To Choose From

In this section, we'll go over the features and availability/pricing plans of various data visualization tools. There are not listed in any specific order.

  1. Tableau
  2. Google Charts
  3. Zoho Analytics
  4. Microsoft Power BI
  5. Qlik Sense
  6. Plotly
  7. Domo
  8. Infogram
  9. D3.js
  10. Databox
  11. Datawrapper
  12. Highcharts

1. Tableau


Tableau landing page

You can use Tableau to access, visualize, and analyze data. It also has a drag and drop feature for a more interactive interface.


  • Real-time data analytics.
  • Offline support.
  • Database and cloud integration.
  • AI and ML powered analytics.
  • Supports integrations for data scientists.
  • Dedicated environment for teams to collaborate and share their work.
  • Slack integration for notifications on data alerts, AI predictions, and analytics for team collaborations.
  • Great tool for non-technical users, as you don't have to write code for basic usage.


Tableau is not a free tool. Although free trails are available, Tableau offers three plans you can choose from — Tableau Creator, Tableau Explorer, and Tableau Viewer at $70, $42, and $15 respectively per month billed annually.

2. Google Charts


Google Charts landing page

Google Charts is a very efficient tool you can use to visualize or display data on a website. It is commonly used with JavaScript.

The documentation guides show how you can create different chart types like bar charts, bubble charts, calendar charts, histograms, maps, pie charts, and so much more.


  • Built for developers. Knowledge of JavaScript is required.
  • Wide range of pre-built chart types to choose from.
  • No plugins required.
  • Real-time data update.
  • Cross-browser compatibility.
  • Cross-platform portability to iOS and Android.
  • Chart types are easily customizable.
  • Can be connected to dashboard to display dynamic charts to users.


Google Charts is a completely free product.

3. Zoho Analytics


Zoho Analytics landing page

Zoho Analytics transforms raw data into insights and dashboards. You can connect different sources files, cloud databases, custom apps, and popular business apps to Zoho.

This product is great for businesses because it lets you prepare, analyze, and visualize data.


  • Automated insights and predictive analytics with the help of AI and ML.
  • Supports low-code and no-code integrations.
  • Supports integration with many popular apps like Twitter, YouTube, Mailchimp, Shopify, and many others.
  • Collaboration feature.
  • Flexible deployment options.
  • Data storytelling feature for presentations using slideshows, web portals, and an embedded AI assistant.


Zoho Analytics offers the following cloud plans:

  • Basic => $22/month billed yearly.
  • Standard => $50/month billed yearly.
  • Premium => $125/month billed yearly.
  • Enterprise => $495/month billed yearly.

The cloud plan has a free 15-day free trial with no credit card required.

The on-premise plan for users who handle all the deployment and hosting themselves offers these options:

Local server:

  • Personal => Free forever.
  • Professional => $30/month billed annually.
  • Minimum of 5 users.


  • Personal => Free forever.
  • Professional => $0.25/hour of AWS usage.
  • Minimum of 5 users.


  • Personal => Free forever.
  • Professional => $0.4/hour Azure infrastructure fee.
  • Minimum of 5 users.


  • Personal => Free forever.
  • Professional => $30/month billed annually.
  • Minimum of 5 users.

4. Microsoft Power BI


Microsoft Power BI landing page

Microsoft Power BI lets you connect to, model, and visualize your data.


  • AI generated answers based on your data.
  • Integration to popular apps and features.
  • Real-time analytics.
  • Secure data analytics.


Microsoft Power Bi has two main plans – Power BI Pro and Power BI Premium at $13.70 and $27.50 per user/month, respectively.

The Power BI Premium plan has a per capacity feature for much larger organizations. This feature starts at $6,858.10 per capacity/month.

5. Qlik Sense


Qlik Sense landing page

Qlik Sense, like the other tools we've listed, has some cool features that makes it a good option for data visualization.


  • AI generated insights.
  • Automated data preparation.
  • Real-time data analytics.
  • Interactive dashboards.
  • Fully customizable analytics.


Qlik Sense Business plan starts at $30 per user/month billed annually. Their Enterprise and client-managed plans can be assessed by contacting their sales team.

6. Plotly


Plotly landing page

Plotly is a low-code tool for building data visualization apps using the Python programming language.


  • Knowledge of Python is required.
  • This product is open source and has an active community.
  • Can be used offline.
  • Open source graphing library for creating line plots, area charts, bar charts, histograms, and more.


Plotly is free and open source.

It also has the Dash Enterprise feature which comes with automated delivery of reports, alerts, and an app manager. This feature is not free.

7. Domo


Domo landing page


  • Real-time data analytics.
  • Interactive dashboards.
  • Data sharing.
  • Data apps built for businesses.


According to Domo:

Pricing is based on several components related to your usage of the platform, including data storage, data refresh rates, volume of data queries, and the number of users...

While it's not known exactly what their pricing plans are, you can reach out to them through their pricing page to find out more.

8. Infogram


Infogram landing page

Inforgram is a great tool for creating infographics, reports, dashboards, slides, social media posts, email headers, and more easily.


  • Interactive charts.
  • Real-time team collaboration.
  • Access to project version history.
  • Great for creating and tracking social media content.
  • Custom tracking links.


Infogram has the following plans:

  • Basic => Free forever.
  • Pro => $19/month.
  • Business => $67/month.
  • Team => 149/month.
  • Enterprise => Contact the Infogram team through their pricing page.

9. D3.js


D3.js landing page

D3.js is a JavaScript library for manipulating and visualizing data on the web using HTML, SVG, and CSS.


  • Knowledge of HTML, CSS, SVG, and JavaScript is required.
  • Gallery of different code examples for creating infographics.
  • Good documentation for learning how to use the library.


D3.js is free and open source.

10. Databox


Databox landing page

Databox is primarily a dashboard tool. It can be used to track and visualize data from any source.


  • Automated reports.
  • Alerts and notifications.
  • Supports integration popular apps and platforms.
  • Customizable templates.


Databox has a free-forever plan that comes with 3 data source connections, all standard features and over 60 integrations.

Here are the other plans:

  • Starter => $72/month.
  • Professional => 135/month.
  • Performer => $231/month.

11. Datawrapper


datawrapper landing page

Datawrapper is a good tool for creating visualization for content like articles, reports, and publications.


  • No code or design skills required.
  • Live visualization update from data source.
  • Responsive on different devices.
  • PNG, SVG, PDF export.
  • Print-ready graphics.
  • Customizable charts.
  • Team collaboration through shared folder, Slack, and Teams integration.
  • Wide range of visualizations to choose from.


Datawrapper has the following plans:

  • Free plan.
  • Custom => $599/month.
  • Enterprise => Contact the Datawrapper team through their pricing page.

12. Highcharts


Highcharts landing page

Highcharts makes it easy for web and mobile developers to create charts.

Built on JavaScript and TypeScript, Highcharts' libraries work with any back-end database, and can be used for Javascript, Angular, React, VueJS, iOS, R, .NET, Python, Java, Android projects.


  • Supports popular tech stacks.
  • Easily customizable.
  • Documentation and learning resources.


Highcharts has the JS, Stock, Maps, and Gantt plans with the following pricing features:

  • Web => $152 per seat annually.
  • SaaS => $300 per seat annually.
  • SaaS+ => $750 per seat annually.
  • OEM => Contact the Highcharts team.

There is also a Highcharts Editor plan that points to a code repository on GitHub. This is currently in Beta.


If you cant visualize and represent your data graphically, it becomes very difficult to understand as the data grows.

There are so many data visualization tools you can choose from. Some require coding experience while others don't. There are also free and premium tools.

Choosing the right one all comes down to the features you are looking for and how much you're willing to spend if it's not free.

This article should help you narrow it down by listing some of the popular data visualization tools, their features, and pricing plans.

Thank you for reading!

Original article source at

#datavisualization #tableau #powerbi #qliksense #plotly #domo #infogram #d3js #databox #datawrapper #highcharts

Top 12 Data Visualization Tools You Must Know

CMPlot.jl: Cloudy Mountain Plot Function in Julia

CMPlot.jl - Cloudy Mountain Plot in Julia 

An informative RDI categorical distribution plot inspired by Violin, Bean and Pirate Plots.

(RDI = Raw data + Descriptive statistics + Inferential statistics)

Like Violin plots, it shows smoothed kernel density curves, revealing information which would be hidden in boxplots, for example presence of multiple "peaks" ("modes") in the distribution "mountain".

Like Bean plots, it shows the raw data, drawn as a cloud of points. By default all data points are shown but you can optionally control this and limit the display to a subset of the data.

Like Pirate plots, it marks confidence intervals (either from Student's T or as Bayesian Highest Density Intervals or as interquantile ranges) for the probable position of the true population mean.

Since by default it does not symmetrically mirror the density curves, it allows immediate comparisions of distributions side-by-side.

Elements of a cloudy mountain plot

The plotting function comes with a large number of options, allowing full customization of the results. On the other hand it is also automatic enough to be able to work with only two arguments: a dataframe and the label/symbol of the dataframe column containing your X (the categorical data).


Please refer to the online documentation at

Download and installation

CMPlot is pure Julia code. It has no platform-specific dependencies and should thus work on all platforms. It requires the packages DataFrames Distributions and PlotlyJS. The latest version of CMPlot can be installed simply by typing:

julia> import Pkg; Pkg.add("CMPlot")

(from the official Julia registry) or

julia> ]
(v1.1) pkg> dev

to install from this GitHub page.

Alternatively you can also save CMPlot.jl to your julia loadpath or add the directory where you downloaded it to your load path:

julia> push!(LOAD_PATH, "./")

There is also a version in Python.


Check the jupyter notebook for usage and examples. You can use the jupyter notebook for fast experimentation or download the module and use it in your data mining and data visualization projects. Note: if the version on github is not rendering properly, try this one, via


julia> using CMPlot
julia> using PlotlyJS

 #call the cmplot directly inside a plotly Figure function as:

julia> plot(cmplot(mydataframe,xcol=:xsymbol)...)

 #alternatively get traces and layout as separate variables, so that you can modify them or combine with others before passing them to Figure() function:

julia> traces,layout=cmplot(mydataframe,xcol=:xsymbol)

julia> # [...] do something with traces/layout

julia> plot(traces,layout) # plot it


The only mandatory arguments for CMPlot are a dataframe containing the data and either a string or a list of strings which label the columns containing the discrete independent variables in the dataframe, as shown above in the Quickstart section.

Several additional optional arguments can be specified to customize the result, both in terms of content and of form.

  • xcol: a string or an array of strings, column name(s) of the dataframe that you wish to plot as "x".

This should be the categorical independent variable. If more than one column name is given, the combination of these will be used as "x". See examples for interpretation. e.g. xcol="Species"

  • ycol: a string or an array of strings, column name(s) of the dataframe that you wish to plot as "y". Optional.

These should be the continuous dependent variables. If ycol is not specified, then the function will plot all the columns of the dataframe except those specified in xcol.

e.g. ycol=["Sepal.Length","Sepal.Width"] would plot sepals' length and width as a function of the flower species

  • orientation: 'h' | 'v', default is 'h'

Orientation of the plot (horizontal or vertical)

  • xsuperimposed: boolean, default is false

The default behaviour is to plot each value of the categorical variable (or each combination of values for multiple categorical variables) in a separate position. Set to true to superimpose the plots. This is useful in combination with "side='alt'" to create asymmetrical plots and comparing combinations of categorical variables (e.g. Married + Gender ~ Wage).

  • xlabel: string or list of strings

Override for labelling (and placing) the plots of the categorical variables. Only relevant when using xsuperimposed

  • ylabel: string or list of strings

Override for labelling the dependent variables. If not specified, the labels for the dataframe ycol are used.

  • title: string

If not specified, the plot title will be automatically created from the names of the variables plotted.

e.g. title="Length of petals for the three species"

  • side: 'pos' | 'neg' | 'both' | 'alt', default is 'alt'

'pos' would create kernel density curves rising towards the positive end of the axis, 'neg' towards the negative, 'both' creates symmetric curves (like violin/bean/pirate plots). 'alt' will alternate between 'pos' and 'neg' in case where multiple ycol are plotted.

e.g. side='both'

  • altsidesflip: boolean, default is false

Set to true to flip the order of alternation between sides for the kernel density curves. Only relevant when side='alt'

  • ycolorgroups: boolean, default is true

Set to false to have the function assign a separate colour when plotting different values of the categorical variable. Leave as true if all should be coloured the same.

  • spanmode: 'soft' | 'hard', default is 'soft'

Controls the rounding of the kernel density curves or their sharp drop at their extremities. With 'hard' the span goes from the sample's minimum to its maximum value and no further.

  • pointsoverdens: boolean, default is false

Set to true to plot the raw data points over the kernel density curves. This is obviously the case when side='both', but otherwise by default points are plotted on the opposite side.

  • showpoints: boolean, default is true

Set to false to avoid plotting the cloud of data points

  • pointsopacity: float, range 0-1, default is 0.4

The default is to plot the data points at 40% opacity. 1 would make points completely opaque and 0 completely transparent (in that case you'd be better served by setting showpoints to false).

  • inf: 'hdi' | 'ci' | 'iqr' | 'none', default is 'hdi'

To select the method to use for calculating the confidence interval for the inference band around the mean. 'hdi' for Bayesian Highest Density Interval, 'ci' for Confidence Interval based on Student's T, 'iqr' for Inter Quantile Range. Use 'none' to avoid plotting the inference band.

  • conf_level: float, range 0-1, default is 0.95

Confidence level to use when inf='ci', credible mass for inf='hdi'

  • hdi_iter: integer, default is 10000

Iterations to use when performing Bayesian t-test when inf='hdi'

  • showboxplot: boolean, default is true

Set to false to avoid displaying the mini boxplot

  • markoutliers: boolean, default is true

Set to false to avoid marking the outliers

  • pointshapes: array of strings

You can specify manually which symbols to use for each distribution plotted. If not specified, a random symbol is chosen for each distribution.

  • pointsdistance: float, range 0-1, default is 0.6

Distance at which data points will be plotted, measured from the base of the density curve. 0 is at the base, 1 is at the top.

  • pointsmaxdisplayed: integer, default is 0

This option sets the maximum number of points to be drawn on the graph. The default value '0' corresponds to no limit (plot all points). This option can be useful when the data amount is massive and would prove inefficient or inelegant to plot.

  • colorrange: integer, default is nothing

By default, the distribution will be coloured independently, with the colours automatically chosen as needed for a single plot, maximising the difference in hue across the colour spectrum. You can override this by specifying a number to accomodate. This is useful when joining different plots together. E.g. if the total number of colours to be accomodating, after joining two plots, would equal 4, then set colorrange=4

  • colorshift: integer, default is 0

This option is used in combination with colorrange to skip a certain amount of colours when they are to be assigned to the distributions to be plotted. This is useful when joining different plots together, to avoid having distributions plotted with the same colour.


  • Hintze, J. L., Nelson, R. D. (1998). Violin plots: A box plot-density trace synergism. The American Statistician 52, 181–184.
  • Kampstra, P. (2008). Beanplot: A boxplot alternative for visual comparison of distributions. Journal of Statistical Software 28, 1–9.
  • Phillips, N. (2017). YaRrr! The pirate's guide to R. The Observer.


CMPlot is licensed under the GNU Affero General Public License.

(c) Copyright Giuseppe Insana, 2019-

Download Details:

Author: G-insana
Source Code: 
License: AGPL-3.0 license

#julia #function #plotly 

CMPlot.jl: Cloudy Mountain Plot Function in Julia
Nat  Grady

Nat Grady


Plotly.R: an interactive Graphing Library for R

An R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js.


Install from CRAN:


Or install the latest development version (on GitHub) via {remotes}:


Getting started

Web-based ggplot2 graphics

If you use ggplot2, ggplotly() converts your static plots to an interactive web-based version!

g <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon") + 
  xlim(1, 6) + ylim(40, 100)


By default, ggplotly() tries to replicate the static ggplot2 version exactly (before any interaction occurs), but sometimes you need greater control over the interactive behavior. The ggplotly() function itself has some convenient “high-level” arguments, such as dynamicTicks, which tells plotly.js to dynamically recompute axes, when appropriate. The style() function also comes in handy for modifying the underlying trace attributes (e.g. hoveron) used to generate the plot:

gg <- ggplotly(g, dynamicTicks = "y")
style(gg, hoveron = "points", hoverinfo = "x+y+text", hoverlabel = list(bgcolor = "white"))


Moreover, since ggplotly() returns a plotly object, you can apply essentially any function from the R package on that object. Some useful ones include layout() (for customizing the layout), add_traces() (and its higher-level add_*() siblings, for example add_polygons(), for adding new traces/data), subplot() (for combining multiple plotly objects), and plotly_json() (for inspecting the underlying JSON sent to plotly.js).

The ggplotly() function will also respect some “unofficial” ggplot2 aesthetics, namely text (for customizing the tooltip), frame (for creating animations), and ids (for ensuring sensible smooth transitions).

Using plotly without ggplot2

The plot_ly() function provides a more direct interface to plotly.js so you can leverage more specialized chart types (e.g., parallel coordinates or maps) or even some visualization that the ggplot2 API won’t ever support (e.g., surface, mesh, trisurf, etc).

plot_ly(z = ~volcano, type = "surface")


Learn more

To learn more about special features that the plotly R package provides (e.g., client-side linking, shiny integration, editing and generating static images, custom events in JavaScript, and more), see You may already be familiar with existing plotly documentation (e.g.,, which is essentially a language-agnostic how-to guide for learning plotly.js, whereas is meant to be more wholistic tutorial written by and for the R user. The package itself ships with a number of demos (list them by running demo(package = "plotly")) and shiny/rmarkdown examples (list them by running plotly_example("shiny") or plotly_example("rmd")). Carson also keeps numerous slide decks with useful examples and concepts.


Please read through our contributing guidelines. Included are directions for opening issues, asking questions, contributing changes to plotly, and our code of conduct.

Download Details:

Author: Plotly
Source Code: 
License: Unknown, MIT licenses found

#r #webgl #datavisualization #plotly 

Plotly.R: an interactive Graphing Library for R
Nat  Grady

Nat Grady


Heatmaply: Interactive Heat Maps for R Using Plotly


Screenshot demo


A heatmap is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by dendrograms. Heatmaps are used in many fields for visualizing observations, correlations, missing values patterns, and more.

Interactive heatmaps allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the heatmap by dragging a rectangle around the relevant area.

This work is based on the ggplot2 and plotly.js engine. It produces similar heatmaps as d3heatmap (or the static heatmap.2 from gplots), with the advantage of more features such as speed (plotly.js is able to handle larger size matrix), sidebar annotation, and the ability to zoom from the dendrogram.

Please submit features requests

This package is still under active development. If you have features you would like to have added, please submit your suggestions (and bug-reports) at:

Latest news

You can see the most recent changes to the package in the file

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.


To install the stable version on CRAN:


To install the latest ("cutting-edge") GitHub version run:

# good packages to install for this to work smoothly:


# You'll need devtools
install.packages.2 <- function (pkg) if (!require(pkg)) install.packages(pkg);
# make sure you have Rtools installed first! if not, then run:
#install.packages('installr'); install.Rtools()

devtools::install_github("ropensci/plotly") # you will probably benefit from the latest version of plotly

And then you may load the package using:



Quick example:

heatmaply(mtcars, k_row = 3, k_col = 2)

For more (interactive) examples see the online vignette on CRAN

There are also more complex biological examples of using heatmaply in the R package heatmaplyExamples (hosted on github). Here are direct links for available examples:

Saving your heatmaply into a file

You can save an interactive version of your heatmaply into an HTML file using the following code:

heatmaply(mtcars, file = "folder/heatmaply_plot.html")

Similar code can be used for saving a static file (png/jpeg/pdf)

# Before the first time using this code you may need to first run:
# webshot::install_phantomjs()
heatmaply(mtcars, file = "folder/heatmaply_plot.png")

Replacing d3heatmap with heatmaply

The package d3heatmap ( is no not actively maintained.

If users are interested in replacing their d3heatmap functions, this is easily done with heatmaply:

  • Function calls d3heatmap::d3heatmap() can be replaced with heatmaply::heatmaply()
  • Users of d3heatmap for shiny applications can use the following functions:
    • d3heatmap::d3heatmapOutput() can be replaced with plotly::plotlyOutput()
    • d3heatmap::renderD3heatmap() can be replaced with plotly::renderPlotly()


This package is thanks to the amazing work done by MANY people in the open source community. Beyond the many people working on the pipeline of R, thanks should go to the people working on ggplot2 (Hadley Wickham, etc.) and plotly (Carson Sievert, etc.). Also, many of the design elements were inspired by the work done on heatmap, heatmap.2 and d3heatmap, so special thanks goes to the R core team, Gregory R. Warnes, and Joe Cheng from RStudio. The dendrogram side of the package is based on the work in dendextend, in which special thanks should go to Andrie de Vries for his original work on bringing dendrograms to ggplot2 (which evolved into the richer ggdend objects, as implemented in dendextend).

The work on heatmaply was done by Tal Galili, Alan O'Callaghan, and Jonathan Sidi (mostly on shinyHeatmaply).

Funding: This work was supported in part by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project).

How to cite the heatmaply package

The methods within the package can be cited as:

Tal Galili, Alan O'Callaghan, Jonathan Sidi, Carson Sievert; heatmaply: an R package for creating
interactive cluster heatmaps for online publishing, Bioinformatics, , btx657,

A BibTeX entry for LaTeX users is

  author = {{Galili} and {Tal} and {O'Callaghan} and {Alan} and {Sidi} and {Jonathan} and {Sievert} and {Carson}},
  title = {heatmaply: an R package for creating interactive cluster heatmaps for online publishing},
  journal = {Bioinformatics},
  year = {2017},
  doi = {10.1093/bioinformatics/btx657},
  url = {},
  eprint = {},

This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles.


You are welcome to:

Download Details:

Author: Talgalili
Source Code: 

#r #plotly #maps 

Heatmaply: Interactive Heat Maps for R Using Plotly
Monty  Boehm

Monty Boehm


A Julia interface to The Plotting Library & Cloud Services


A Julia interface to the plotting library and cloud services


Simply run Pkg.add("Plotly").


Plotting functions provided by this package are identical to PlotlyJS. Please consult its documentation. In fact, the package depends on PlotlyJS.jl and reexports all the methods.

For example, this will display a basic scatter plot:

my_plot = plot([scatter(x=[1,2], y=[3,4])], Layout(title="My plot"))

Using the Plotly cloud

New user signup

Find your username and API key in the Plotly settings.


julia> Plotly.signin("username","your api key")
PlotlyAccount("username","your api key")

Note: you may also specify your session endpoints using sign in as follows:

julia> Plotly.signin("username","your api key",Dict("plotly_domain"=> "your_plotly_base_endpoint", "plotly_api_domain"=> "your_plotly_api_endpoint"))

Saving your credentials

julia> Plotly.set_credentials_file(Dict("username"=>"your_user_name","api_key"=>"your_api_key"))

Note: your credentials will be saved within /YOUR_HOME_DIR/.plotly/.credentials

Saving your endpoint configuration

julia> Plotly.set_config_file(Dict("plotly_domain"=> "your_plotly_base_endpoint", "plotly_api_domain"=> "your_plotly_api_endpoint"))

Note: your configuration will be saved within /YOUR_HOME_DIR/.plotly/.config

Saving a plot to the cloud

Use the post function to upload a local plot to the Plotly cloud:

> my_plot  = plot([scatter(y=[1,2])])
> remote_plot = post(my_plot)

Visiting in a browser will show the plot.

Download a plot from the cloud

Use the download function with a remote plot object to download a plot stored on the Plotly cloud to a local Plot object:

local_plot = download(RemotePlot(""))
# or equivalently, local_plot = download_plot("")

Working with plotlyjs() backend of Plots.jl

A plot created by Plots.jl with plotlyjs() backend has the type Plot{Plots.PlotlyJSBackend()}. To use the Plotly.jl interfeces, the plot object needs to be converted by

my_plot = Plots.plotlyjs_syncplot(plots_plot)

The object my_plot can be handed to the Cloud API descrived above.


PlotlyJS.jl , which provides the large majority of the functionality of this package, is developed primarily by Spencer Lyon.

This package, which adds to PlotlyJS.jl the functionality for interacting with the Plotly cloud, is developed by Jon Malmaud and others.


Please do! This is an open source project. Check out the issues or open a PR!

We want to encourage a warm, welcoming, and safe environment for contributing to this project. See the code of conduct for more information.

Author: Plotly
Source Code: 
License: View license

#julia #cloud #plotly 

A Julia interface to The Plotting Library & Cloud Services
Monty  Boehm

Monty Boehm


PlotlyJS.jl: Julia Wrapper/API for Plotly.js


Julia interface to plotly.js visualization library.

This package constructs plotly graphics using all local resources. To interact or save graphics to the Plotly cloud, use the Plotly package.

Check out the docs!


If you intend to use the Electron display or any of its features (recommended) you will need to enter the following at the Julia REPL:

using Blink

Note that this is a one time process.

Also, if you have issues building this package because of installation of the MbedTLS package please see this issue.

Author: EricForgy
Source Code: 
License: View license

#julia #api #plotly 

PlotlyJS.jl: Julia Wrapper/API for Plotly.js
Gordon  Taylor

Gordon Taylor


Plotly.js: Open-source JavaScript charting library behind Plotly, Dash

Plotly.js is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as and Plotly.R).

Plotly.js can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.


Contact us for Plotly.js consulting, dashboard development, application integration, and feature additions.

Load as a node module

Install a ready-to-use distributed bundle

npm i --save plotly.js-dist-min

and use import or require in node.js

// ES6 module
import Plotly from 'plotly.js-dist-min'

// CommonJS
var Plotly = require('plotly.js-dist-min')

You may also consider using plotly.js-dist if you prefer using an unminified package.

Load via script tag

The script HTML element

In the examples below Plotly object is added to the window scope by script. The newPlot method is then used to draw an interactive figure as described by data and layout into the desired div here named gd. As demonstrated in the example above basic knowledge of html and JSON syntax is enough to get started i.e. with/without JavaScript! To learn and build more with plotly.js please visit plotly.js documentation.

    <script src=""></script>
    <div id="gd"></div>

        Plotly.newPlot("gd", /* JSON object */ {
            "data": [{ "y": [1, 2, 3] }],
            "layout": { "width": 600, "height": 400}

Alternatively you may consider using native ES6 import in the script tag.

<script type="module">
    import ""
    Plotly.newPlot("gd", [{ y: [1, 2, 3] }])

Fastly supports Plotly.js with free CDN service.

Un-minified versions are also available on CDN

While non-minified source files may contain characters outside UTF-8, it is recommended that you specify the charset when loading those bundles.

<script src="" charset="utf-8"></script>

Please note that as of v2 the "plotly-latest" outputs (e.g. will no longer be updated on the CDN, and will stay at the last v1 patch v1.58.5. Therefore, to use the CDN with plotly.js v2 and higher, you must specify an exact plotly.js version.


You could load either version two or version three of MathJax files, for example:

<script src=""></script>
<script src=""></script>

When using MathJax version 3, it is also possible to use chtml output on the other parts of the page in addition to svg output for the plotly graph. Please refer to devtools/test_dashboard/index-mathjax3chtml.html to see an example.


There are two kinds of plotly.js bundles:

  1. Complete and partial official bundles that are distributed to npm and the CDN, described in the dist README.
  2. Custom bundles you can create yourself to optimize the size of bundle depending on your needs. Please visit CUSTOM_BUNDLE for more information.

Alternative ways to load and build plotly.js

If your library needs to bundle or directly load plotly.js/lib/index.js or parts of its modules similar to index-basic in some other way than via an official or a custom bundle, or in case you want to tweak the default build configurations of browserify or webpack, etc. then please visit


Official plotly.js documentation is hosted at

These pages are generated by the Plotly graphing-library-docs repo built with Jekyll and publicly hosted on GitHub Pages. For more info about contributing to Plotly documentation, please read through contributing guidelines.

Bugs and feature requests

Have a bug or a feature request? Please open a Github issue keeping in mind the issue guidelines. You may also want to read about how changes get made to Plotly.js


Please read through our contributing guidelines. Included are directions for opening issues, using plotly.js in your project and notes on development.

Notable contributors

Plotly.js is at the core of a large and dynamic ecosystem with many contributors who file issues, reproduce bugs, suggest improvements, write code in this repo (and other upstream or downstream ones) and help users in the Plotly community forum. The following people deserve special recognition for their outsized contributions to this ecosystem:

Alex C. Johnson@alexcjohnson Active, Maintainer
Mojtaba Samimi@archmoj@solarchvisionActive, Maintainer
Antoine Roy-Gobeil@antoinerg Active, Maintainer
Nicolas Kruchten@nicolaskruchten@nicolaskruchtenActive, Maintainer
Jon Mease@jonmmease@jonmmeaseActive
Étienne Tétreault-Pinard@etpinard@etpinardHall of Fame
Mikola Lysenko@mikolalysenko@MikolaLysenkoHall of Fame
Ricky Reusser@rreusser@rickyreusserHall of Fame
Dmitry Yv.@dy@DimaYvHall of Fame
Robert Monfera@monfera@monferaHall of Fame
Robert Möstl@rmoestl@rmoestlHall of Fame
Nicolas Riesco@n-riesco Hall of Fame
Miklós Tusz@mdtusz@mdtuszHall of Fame
Chelsea Douglas@cldougl Hall of Fame
Ben Postlethwaite@bpostlethwaite Hall of Fame
Chris Parmer@chriddyp Hall of Fame
Alex Vados@alexander-daniel Hall of Fame

Copyright and license

Code and documentation copyright 2021 Plotly, Inc.

Code released under the MIT license.


This project is maintained under the Semantic Versioning guidelines.

See the Releases section of our GitHub project for changelogs for each release version of plotly.js.


  • Follow @plotlygraphs on Twitter for the latest Plotly news.
  • Implementation help may be found on (tagged plotly-js) or on Stack Overflow (tagged plotly).
  • Developers should use the keyword plotly on packages which modify or add to the functionality of plotly.js when distributing through npm.

Author: plotly
Source Code: 
License: MIT license

#javascript #d3 #plotly #visualization #datavisualization 

Plotly.js: Open-source JavaScript charting library behind Plotly, Dash

Plotly Dash Tutorial | Build Web Apps in Python

A growing number of viewers are looking for high quality, professional content on Dash, which is hard to find. I am trying to fill that gap and help you grow in the area of analytic web apps in Python.

My goal is to make this a sustainable project for myself and my viewers. This repository and my Charming Data channel is a 100% member-supported educational channel. Your support would mean a lot to me 🙏


This Repository is dedicated to teaching Dash and Plotly to anyone that is interested. Dash is a powerful platform that can benefit anyone that works with data: analytical consultants, data analysts, professors, business owners, financial analysts, or those that work in marketing, social media, the medical field... If you work with data, Dash Plotly is a great tool to have.


I recommend installing pandas, since you will most likely use it.

$ pip install pandas

Then, just install Dash. Plotly comes with Dash

$ pip install dash

If you'd like to control the version of Plotly installed, you can do for example:

$ pip install plotly==5.7.0

To Get Help

This is a wonderful community of people dedicated to supporting others learning Dash. You can find me there as well under the name CharmingData.

Execute Code in Browser

If you prefer to run the code of this repository directly online instead of on your computer, paste my Workspace link into your browser and follow the gif below.

Workspace Snapshot



GitHub Code: 

Learning resources: 

Exercises to practice Dash: 

Get my Dash Book: 


#python #plotly 

Plotly Dash Tutorial | Build Web Apps in Python
Helene  Ward

Helene Ward


How to Build an Analytical Dashboard using Plotly Dash & Python

[COURSE PRE-LAUNCH] YOLOX PRO - Build your Own Computer Vision Dashboard Course. We will show you how to build your own production ready Full Stack Traffic Flow Analytics Dashboard from scratch.

💻COLAB Notebook files + Dataset-
⭐️PREORDER YOLOX DASH Course [Early Bird Price] -

Are you wanting to build a full end to end Computer Vision Web App or Dashboard but just don’t where to get started? Well, if this is you, then watch till the end, where I will reveal my 5 step process as to how you can develop your own Computer Vision Product which you can also use in production?

Watch the video till the end to get a special Early Bird Discount.

#tensorflow #opencv #python #plotly  #computervision 

How to Build an Analytical Dashboard using Plotly Dash & Python
Jacob Banks

Jacob Banks


How to Build Interactive Charts with Plotly and Python

Data Visualization with Python

Data Visualization is one of the indispensable topic in Data Science and Machine learning. It is process of creating interactive visuals to understand trends, variations, and derive meaningful insights from the data. We all know that Data is the new oil. Likewise if oil is not processed it is of no use and to derive relevant insights from data for making critical business decisions, it should be in cleaned and processed.

Data visualization make this task little bit more handy and fast. With the help of visual charts and graph, we can easily find out the outliers, nulls, random values, distinct records, the format of dates, sensibility of spatial data, and string and character encoding and much more.

In this learning course, you will be learning different charts to represent different kind of data like categorical, numerical, spatial, textual and much more.

  • Bar Charts (Horizontal and Vertical)
  • Line Charts
  • Pie Charts
  • Donut Charts
  • Scatter Charts
  • Grouped Bar Chart (Horizontal and Vertical)
  • Segmented Bar Chart (Horizontal and Vertical)
  • Time and series Chart
  • Sunburst Chart
  • Candlestick Chart
  • OHLC Charts
  • Bubble Charts
  • Dot Charts
  • Multiple Line Charts and so on.

Most of the time data scientists pay little attention to graphs and focuses only on the numerical calculations which at times can be misleading. Data visualization is much crucial step to follow to achieve goals either in Data Analytics or Data Science to get meaningful insights or in machine learning to build accurate model.

#datavisualization #python #plotly

How to Build Interactive Charts with Plotly and Python
Poppy Cooke

Poppy Cooke


Data Visualization with Plotly

In this tutorial, you'll learn about how to Data Visualization by using Plotly. Plotly is the best tool in Python for Data Visualization. Bar charts in Plotly, Plotly for Python, Histogram using Plotly for Python, Distribution plots using Plotly for Python, Scatter plots using Plotly, Line charts using Plotly, Box and whisker plots in Plotly for python, Subplots using Plotly, Import image into a Google colab, How to use plotly in google colab 

Along the way, you will learn about following:

- Bar charts in Plotly (0:00)
- Plotly for Python(19:45)
- Histogram using Plotly for Python (20:55)
- Distribution plots using Plotly for Python (33:00)
- Scatter plots using Plotly (42:00)
- Line charts using Plotly (49:00)
- Box and whisker plots in Plotly for python (56:00)
- Subplots using Plotly (1:06:00)
- Import image into a Google colab (1:15:00)
- How to use plotly in google colab (1:18:00)

#datavisualization #plotly #python

Data Visualization with Plotly

Data Visualization with Microsoft Power BI, Python and Plotly

Learn to build interactive charts with Plotly, Python and Power BI. Learn to create various types of Data Visualization charts using Microsoft Power BI, Python and Plotly.

There are a wide range of professionals who require data visualization skills to plot various charts to find critical insights from the dataset. From Marketing and sales professionals to Developers, Analysts and Data Scientists, a large number of professionals require some kind of knowledge to adequately model and represent data into creative visuals that makes it easy and intuitive to understand the complex data value in form for comparable and easy to understand visual charts. Most of the basic charts such as bar, line, pie, tree map and other charts in Power BI and other visualization software are just inefficient to represent various kinds of data with complex information. Professionals just don't rely on few basic charts, rather they could create some custom chart to solve complex problem. Most of the custom or advanced visualization charts can be created by writing few lines of python code.

In this course, you will be learning following concepts and visualization charts using Python libraries such as Pandas, Matplotlib and Seaborn:

  •    Installing python packages and defining path
  •    Creating a Line chart with matplotlib
  •    Putting labels and creating dashed scatterplot
  •    Violin chart with seaborn
  •    More on Violin chart
  •    Stripplot
  •    Boxplot
  •    Lmplot or align plot

Data visualization make this task little bit more handy and fast. With the help of visual charts and graph, we can easily find out the outliers, nulls, random values, distinct records, the format of dates, sensibility of spatial data, and string and character encoding and much more.

Moreover, you will be learning different charts to represent different kind of data like categorical, numerical, spatial, textual and much more.

  •    Bar Charts (Horizontal and Vertical)
  •    Line Charts
  •    Pie Charts
  •    Donut Charts
  •    Scatter Charts
  •    Grouped Bar Chart (Horizontal and Vertical)
  •    Segmented Bar Chart (Horizontal and Vertical)
  •    Time and series Chart
  •    Sunburst Chart
  •    Candlestick Chart
  •    OHLC Charts
  •    Bubble Charts
  •    Dot Charts
  •    Multiple Line Charts and so on.

Most of the time data scientists pay little attention to graphs and focuses only on the numerical calculations which at times can be misleading. Data visualization is much crucial step to follow to achieve goals either in Data Analytics or Data Science to get meaningful insights or in machine learning to build accurate model. The skills you learn in this course can be used in various domains related to data science and data analytics to business intelligence and machine learning.

What you'll learn:

  • Learn to create interactive charts with Plotly
  • Learn to build dummy datasets like Fake Stock market price simulator
  • Learn to create vertical and horizontal bar charts
  • Learn to create vertical and horizontal grouped and stacked bar charts
  • Learn to create scatter charts
  • Learn to create line charts
  • Learn to create time series charts
  • Learn to create pie, donut and sunburst charts
  • Learn to create multiple line charts
  • Learn to create bubble and dot charts


#datavisualization #python #plotly #powerbi

Data Visualization with Microsoft Power BI, Python and Plotly
Jamison  Fisher

Jamison Fisher


Pandas Cufflinks: Productivity Tools for Plotly & Pandas


This library binds the power of plotly with the flexibility of pandas for easy plotting.

This library is available on

This tutorial assumes that the plotly user credentials have already been configured as stated on the getting started guide.

3D Charts

Release Notes


Support for Plotly 4.x
Cufflinks is no longer compatible with Plotly 3.x


Support for Plotly 3.0


New iplot helper. To see a comprehensive list of parameters

# For a list of supported figures
# Or to see the parameters supported that apply to a given figure try'scatter')'candle') #etc


Removed dependecies on ta-lib. This library is no longer required. All studies have be rewritten in Python.


  • QuantFigure is a new class that will generate a graph object with persistence. Parameters can be added/modified at any given point.

This can be as easy as:

qf=cf.QuantFig(df,title='First Quant Figure',legend='top',name='GS')


  • Technical Analysis Studies can be added on demand.

Technical Analysis


rangeslider to display a date range slider at the bottom

  • cf.datagen.ohlc().iplot(kind='candle',rangeslider=True)

rangeselector to display buttons to change the date range displayed

  • cf.datagen.ohlc(500).iplot(kind='candle', rangeselector={ 'steps':['1y','2 months','5 weeks','ytd','2mtd','reset'], 'bgcolor' : ('grey',.3), 'x': 0.3 , 'y' : 0.95})

Customise annotions, with fontsize,fontcolor,textangle

  • Label mode
    • cf.datagen.lines(1,mode='stocks').iplot(kind='line', annotations={'2015-02-02':'Market Crash', '2015-03-01':'Recovery'}, textangle=-70,fontsize=13,fontcolor='grey')
  • Explicit mode
    • cf.datagen.lines(1,mode='stocks').iplot(kind='line', annotations=[{'text':'exactly here','x':'0.2', 'xref':'paper','arrowhead':2, 'textangle':-10,'ay':150,'arrowcolor':'red'}])


  • Figure.iplot() to plot figures
  • New high performing candle and ohlc plots
    • cf.datagen.ohlc().iplot(kind='candle')


  • 'cf.datagen.choropleth()' to for sample choropleth data.
  • 'cf.datagen.scattergeo()' to for sample scattergeo data.
  • Support for choropleth and scattergeo figures in iplot
  • 'cf.get_colorscale' for maps and plotly objects that support colorscales


  • xrange, yrange and zrange can be specified in iplot and getLayout
    • cf.datagen.lines(1).iplot(yrange=[5,15])
  • layout_update can be set in iplot and getLayout to explicitly update any Layout value


  • Support for Python 3


See the IPython Notebook

Support for pie charts

  • cf.datagen.pie().iplot(kind='pie',labels='labels',values='values')

Generate Open, High, Low, Close data

  • datagen.ohlc()

Candle Charts support

  • ohlc=cf.datagen.ohlc()

OHLC (Bar) Charts support

  • ohlc=cf.datagen.ohlc()

Support for logarithmic charts ( logx | logy )

  • df=pd.DataFrame([x**2] for x in range(100))

Support for MulitIndex DataFrames

Support for Error Bars ( error_x | error_y )

  • cf.datagen.lines(1,5).iplot(kind='bar',error_y=[1,2,3.5,2,2])
  • cf.datagen.lines(1,5).iplot(kind='bar',error_y=20, error_type='percent')

Support for continuous error bars

  • cf.datagen.lines(1).iplot(kind='lines',error_y=20,error_type='continuous_percent')
  • cf.datagen.lines(1).iplot(kind='lines',error_y=10,error_type='continuous',color='blue')

Technical Analysis Studies for Timeseries (beta)

Simple Moving Averages (SMA)

  • cf.datagen.lines(1,500).ta_plot(study='sma',periods=[13,21,55])

Relative Strength Indicator (RSI)

  • cf.datagen.lines(1,200).ta_plot(study='boll',periods=14)

Bollinger Bands (BOLL)

  • cf.datagen.lines(1,200).ta_plot(study='rsi',periods=14)

Moving Average Convergence Divergence (MACD)

  • cf.datagen.lines(1,200).ta_plot(study='macd',fast_period=12,slow_period=26, signal_period=9)


  • Support of offline charts
    • cf.go_offline()
    • cf.go_online()
    • cf.iplot(figure,online=True) (To force online whilst on offline mode)
  • Support for secondary axis
    • fig=cf.datagen.lines(3,columns=['a','b','c']).figure()


  • Support for global theme setting
    • cufflinks.set_config_file(theme='pearl')
  • New theme ggplot
    • cufflinks.datagen.lines(5).iplot(theme='ggplot')
  • Support for horizontal bar charts barh
    • cufflinks.datagen.lines(2).iplot(kind='barh',barmode='stack',bargap=.1)
  • Support for histogram orientation and normalization
    • cufflinks.datagen.histogram().iplot(kind='histogram',orientation='h',norm='probability')
  • Support for area plots
    • cufflinks.datagen.lines(4).iplot(kind='area',fill=True,opacity=1)
  • Support for subplots
    • cufflinks.datagen.histogram(4).iplot(kind='histogram',subplots=True,bins=50)
    • cufflinks.datagen.lines(4).iplot(subplots=True,shape=(4,1),shared_xaxes=True,vertical_spacing=.02,fill=True)
  • Support for scatter matrix to display the distribution amongst every series in the DataFrame
    • cufflinks.datagen.lines(4,1000).scatter_matrix()
  • Support for vline and hline for horizontal and vertical lines
    • cufflinks.datagen.lines(3).iplot(hline=[2,3])
    • cufflinks.datagen.lines(3).iplot(hline=dict(y=2,color='blue',width=3))
  • Support for vspan and hspan for horizontal and vertical areas
    • cufflinks.datagen.lines(3).iplot(hspan=(-1,2))
    • cufflinks.datagen.lines(3).iplot(hspan=dict(y0=-1,y1=2,color='orange',fill=True,opacity=.4))


  • Global setting for public charts
    • cufflinks.set_config_file(world_readable=True)


  • Enhanced Spread charts
    • cufflinks.datagen.lines(2).iplot(kind='spread')
  • Support for Heatmap charts
    • cufflinks.datagen.heatmap().iplot(kind='heatmap')
  • Support for Bubble charts
    • cufflinks.datagen.bubble(4).iplot(kind='bubble',x='x',y='y',text='text',size='size',categories='categories')
  • Support for Bubble3d charts
    • cufflinks.datagen.bubble3d(4).iplot(kind='bubble3d',x='x',y='y',z='z',text='text',size='size',categories='categories')
  • Support for Box charts
  • Support for Surface charts
    • cufflinks.datagen.surface().iplot(kind='surface')
  • Support for Scatter3d charts
    • cufflinks.datagen.scatter3d().iplot(kind='scatter3d',x='x',y='y',z='z',text='text',categories='categories')
  • Support for Histograms
    • cufflinks.datagen.histogram(2).iplot(kind='histogram')
  • Data generation for most common plot types
    • cufflinks.datagen
  • Data extraction: Extract data from any Plotly chart. Data is delivered in DataFrame
    • cufflinks.to_df(Figure)
  • Integration with colorlover
    • Support for scales iplot(colorscale='accent') to plot a chart using an accent color scale
    • cufflinks.scales() to see all available scales
  • Support for named colors * iplot(colors=['pink','red','yellow'])

Download Details:
Author: santosjorge
Source Code:
License: MIT License

#pandas  #python #plotly 

Pandas Cufflinks: Productivity Tools for Plotly & Pandas

dash: Analytical Web Apps for Python, R, Julia, and Jupyter


Dash is the most downloaded, trusted Python framework for building ML & data science web apps.

Built on top of Plotly.js, React and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Python code. Read our tutorial proudly crafted ❤️ by Dash itself.

User Guide

Dash Docs on Heroku (for corporate network that cannot access

Open-Source App Gallery With sample code and templates!

App Samples

Sample Dash AppHere’s a simple example of a Dash App that ties a Dropdown to a D3.js Plotly Graph. As the user selects a value in the Dropdown, the application code dynamically exports data from Google Finance into a Pandas DataFrame. This app was written in just 43 lines of code (view the source).
Crossfiltering Dash AppDash app code is declarative and reactive, which makes it easy to build complex apps that contain many interactive elements. Here’s an example with 5 inputs, 3 outputs, and cross filtering. This app was composed in just 160 lines of code, all of which were Python.
Dash App with Mapbox map showing walmart store openingsDash uses Plotly.js for charting. Over 35 chart types are supported, including maps.
Financial report
Dash isn't just for dashboards. You have full control over the look and feel of your applications. Here's a Dash App that's styled to look like a PDF report.

To learn more about Dash, read the extensive announcement letter or jump in with the user guide.

Dash OSS & Dash Enterprise

With Dash Open Source, Dash apps run on your local laptop or workstation, but cannot be easily accessed by others in your organization.

Scale up with Dash Enterprise when your Dash app is ready for department or company-wide consumption. Or, launch your initiative with Dash Enterprise from the start to unlock developer productivity gains and hands-on acceleration from Plotly's team.

ML Ops Features: A one-stop shop for ML Ops: Horizontally scalable hosting, deployment, and authentication for your Dash apps. No IT or DevOps required.

  • App manager Deploy & manage Dash apps without needing IT or a DevOps team. App Manager gives you point & click control over all aspects of your Dash deployments.
  • Kubernetes scaling Ensure high availability of Dash apps and scale horizontally with Dash Enterprise’s Kubernetes architecture. No IT or Helm required.
  • No code auth Control Dash app access in a few clicks. Dash Enterprise supports LDAP, AD, PKI, Okta, SAML, OpenID Connect, OAuth, SSO, and simple email authentication.
  • Job Queue The Job Queue is the key to building scalable Dash apps. Move heavy computation from synchronous Dash callbacks to the Job Queue for asynchronous background processing.

Low-Code Features: Low-code Dash app capabilities that supercharge developer productivity.

  • Design Kit Design like a pro without writing a line of CSS. Easily arrange, style, brand, and customize your Dash apps.
  • Snapshot Engine Save & share Dash app views as links or PDFs. Or, run a Python job through Dash and have Snapshot Engine email a report when the job is done.
  • Dashboard Toolkit Drag & drop layouts, chart editing, and crossfilter for your Dash apps.
  • Embedding Natively embed Dash apps in an existing web application or website without the use of IFrames.

Enterprise AI Features: Everything that your data science team needs to rapidly deliver AI/ML research and business initiatives.

  • AI App Marketplace Dash Enterprise ships with dozens of Dash app templates for business problems where AI/ML is having the greatest impact.
  • Big Data for Pything Connect to Python's most popular big data back ends: Dask, Databricks, NVIDIA RAPIDS, Snowflake, Postgres, Vaex, and more.
  • GPU & Dask Acceleration Dash Enterprise puts Python’s most popular HPC stack for GPU and parallel CPU computing in the hands of business users.
  • Data Science Workspaces Be productive from Day 1. Write and execute Python, R, & Julia code from Dash Enterprise's onboard code editor.

See to get in touch.


Download Details: 
Author: plotly
Source Code: 
License: MIT

#react #python #flask #jupyter #julia #webapp #plotly #datavisualization #dash #machinelearning

dash: Analytical Web Apps for Python, R, Julia, and Jupyter

How to Build Interactive Timeseries Plots using Plotly & Python

In this tutorial, we'll learn Top 4 Python Libraries To Build interactive Timeseries Plots using Plotly, we will explore two interactive elements for timeseries visualization -

  • Dropdown menus let you toggle between different series in the same plot
  • Date range sliders allowing you to observe trends between specific periods

#python #plotly 

How to Build Interactive Timeseries Plots using Plotly & Python