Update: version 4.9 has been released since this was posted.
I’m excited to announce that Plotly.py 4.5 is now available for download via
conda! For up-to-date installation instructions (including the extra required steps for JupyterLab!) please see our Getting Started documentation page and if you run into trouble, check out our Troubleshooting Guide.
What’s new in Plotly.py 4.5
Our changelog has lots of play by play, but here are the highlights:
Legend Titles (on by default in Plotly Express)
We’ve finally implemented a long-requested feature: first-class legend titles! These are configurable under
layout.legend.title, as one would expect, and we’ve modified Plotly Express to use these by default. This means that instead of seeing wide legend entries with e.g.
continent=Europe etc you will now see a cleaner, smaller and less repetitive legend with a title of
continent and legend entries for
Europe. Check out the image below or have a scroll through our Plotly Express gallery to see how much nicer this makes everything look.
GeoJSON Choropleth Maps and Mapping Improvements
go.Choropleth now accept a
geojson argument just like the new
go.Choroplethmapbox we introduced last summer. This means that you can now supply your own geometry information to non-tile-map choropleth maps, instead of relying on Plotly’s built-in country and state maps. Some other new mapping features include:
featureidkeyattribute to let you use GeoJSON objects whose identifiers are in the
propertiesobject of a feature, rather than always and only using the
idfield. (This was added to the Mapbox choropleth as well.)
layout.geoto make it easier to position these new choropleth maps.
layout.geoto make it easier to style these new choropleth maps.
What does this all mean? It means you can now make any of these maps with a single chained Python statement!
A new Sunburst and Treemap API in Plotly Express
px.treemap() functions in Plotly Express now support a new argument called
path which behaves a bit like
dimensions in our other multi-dimensional data-exploration functions like
px.scatter_matrix(). It’s an easier way to create sunbursts and treemaps from “rectangular” data in data frames which have one column per level in a hierarchy. This makes sunbursts and treemaps much easier to use for exploring multi-dimensional datasets.
A common scenario for which this API is a great fit is when you want to visualize a ratio across a hierarchy or a set of variables. For example, on a restaurant bill, the ratio between the tip and the total bill is the tip percentage:
tip_pct = tip/total_bill. With the new API, we can easily (one function call!) visualize the built-in
px.data.tips() dataset, scaling each sector by the
total_bill and coloring each sector by the
total_bill-weighted average of the
tip_pct to highlight certain patterns:
Sunbursts and Treemaps Bugfix:
A frequently-reported issue with sunbursts and treemaps is that when setting the
branchvalues attribute to
Uniform Text & Text Orientation
By default, when displaying text with bar-like traces (bar, funnel and waterfall charts) or part-of-whole traces like pie and funnel-area charts as well as sunbursts and treemaps, Plotly.py will scale the text per bar or per sector so as to make each text label fit. This is helpful for seeing all text labels but can result in some odd or distracting variations in text sizes. With the new
layout.uniformtext options, you can now force the text to be the same size across similar traces, so that all the bar labels or all the sunburst labels end up having the same font-size.
In terms of text orientation, by default Plotly.py will try various text orientations within a pie or sunburst sector to see how it can make the text biggest/most-legible. Again this can be distracting or unappealing in certain cases, so the new
insidetextorientation attribute lets you specify whether you want your text to be always
Together these two attributes can be used to create very attractive, regular-looking bar, waterfall, funnel, pie, sunburst and treemap charts!
New Box Plot Options
There are many ways to calculate the quartiles of a dataset, and now you can choose among three different quartile-calculation algorithms when using Plotly.py box plots! To quote our Figure Reference documentation for the new
quartilemethod attribute on
Sets the method used to compute the sample’s Q1 and Q3 quartiles. The “linear” method uses the 25th percentile for Q1 and 75th percentile for Q3 as computed using method #10 (listed on http://www.amstat.org/publications/jse/v14n3/langford.html). The “exclusive” method uses the median to divide the ordered dataset into two halves if the sample is odd, it does not include the median in either half - Q1 is then the median of the lower half and Q3 the median of the upper half. The “inclusive” method also uses the median to divide the ordered dataset into two halves but if the sample is odd, it includes the median in both halves - Q1 is then the median of the lower half and Q3 the median of the upper half.
Here is a visualization of the three methods on the same dataset of integers from 1 to 9:
In addition to these enhancements, it is now possible to explicitly specify all the parameters of a box plot, for when you want to do the quartile and other calculations externally to the visualization.
These were two sponsored features and we heartily thank our sponsors for supporting open-source software development and allowing us to make these features available to everyone else in the world for free .
Cell Intensity Values in 3d Meshes
You can now define whether mesh intensity (color) values are defined on vertices (in which case the color is interpolated between vertices), or on cells (for flat colors on cells), thanks to the new
intensitymode attribute of
go.Mesh3d. This means you can now avoid color-interpolation within triangles in meshes.
Here is the same image with
intensitymode set to
And then again with
intensitymode set to
Mapbox Zoom Buttons
Tile-based Mapbox maps now have +/- zoom buttons in the mode-bar in addition to supporting the standard “scroll wheel to zoom” interaction pattern for tile maps.
On the topic of interactions with Mapbox-powered maps: a little-known fun fact is that clicking-and-dragging with the right mouse button lets you control the viewing angle in pseudo-3d. Try it today!
Major Documentation Work
Beyond shipping new features, our Graphing Library team has been hard at work improving the Plotly.py documentation:
The built-in color scale reference page has been improved, notably around a nicer way to show cyclical color scales at the bottom.
We have also added a comprehensive README explaining how our documentation is written and built. We thank James Wong (@jingw222) for adding a new example about constrained axes to the axes tutorial, and we hope that the new instructions will help community users to contribute to the improvement of the documentation.
Powered by Plotly.js 1.52.1, perfect for Dash 1.8.0
The version of Plotly.js that Plotly.py 4.5 is built on is the same one that’s bundled with the recently-released Dash 1.8.0 so we recommend that if you’re a Dash user you upgrade to both Dash 1.8.0 and Plotly.py 4.5 to get the full benefit of all of these libraries working together.
Get it now!
To sum up: Plotly.py 4.5.0 is out and if you’re excited about any of the above features, head on over to our Getting Started documentation page for full installation instructions, and don’t forget to upgrade your JupyterLab extensions if that is your environment of choice!