Magic Analytics
  • Home
  • Python
    • Pandas
    • Matplotlib
    • Interactive Visualization
    • Folium
  • Spark
    • DataFrame
  • Machine Learning
    • Classification >
      • Logistic Regression
    • Dimension Reduction
    • Model Explaination
  • Blog
  • About

Aries Research Note

Plotly: subplots in figure (Part 2)

10/30/2016

0 Comments

 
The caveat in Part 1 is about Pie chart: if one trying to replace any go.Bar or go.Histogram with go.Pie chart, there will be an error showing (plotly version 1.12.9) 

    
Picture
The reason for this is because the "tools.make_subplots" function creates a set of subplots based on different xaxis and yaxis, while go.Pie does not require (and it does not have) an axis property. 

The way to overcome such challenge is to build up the graph from scratch, using "domain" and "anchor".

To explain the concept, take a look at the "layout" in Part 1's example. Each layout axis (x and y) is attached with a specific domain on this figure, with its axis "anchored" with a specific data. subplot-1 is on upper left, so its xaxis domain is [0, 0.45] (left side), and yaxis domain is [0.625, 1] (upper side).
Picture
For Pie plot, since it cannot have axis, the make_subplots approach fake, but following approach still works

    
Picture
I have to say, I hate to use the Annotation as a way to make the subplot's title ... however, I currently didn't find any other way to directly assign subplot title. If you got any better approach to simplify the code in general, feel free to comment on this blog. 
0 Comments



Leave a Reply.

    Author

    Data Magician

    Archives

    October 2017
    April 2017
    November 2016
    October 2016
    September 2016

    Categories

    All
    Git
    Hive
    Machine Learning
    Matplotlib
    Pandas
    Plotly
    Python
    R
    Spark

    RSS Feed

Powered by Create your own unique website with customizable templates.
  • Home
  • Python
    • Pandas
    • Matplotlib
    • Interactive Visualization
    • Folium
  • Spark
    • DataFrame
  • Machine Learning
    • Classification >
      • Logistic Regression
    • Dimension Reduction
    • Model Explaination
  • Blog
  • About