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) 

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).

For Pie plot, since it cannot have axis, the make_subplots approach fake, but following approach still works

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. 

In Matplotlib, subplot can be easily pulled out as following:

However, matplotlib is not quite smart to handle the axis ticks rotation (sometimes they collapse together and hard to visualize), and this could be troublesome for some automatic visualization process. In general, I found plotly offer a better automated layout.

The way subplots in plotly and matplotlib are conceptually different on:
1. (matplotlib) figure --- axes --- artist, so that one figure can contain multiple axes, and each axes has their own set of artist. For example, legend is an artist, and each axes could have its own legend
2. (plotly) the main component in a figure is "data" and "layout", the way subplot works is to create multiple data, put into different axis. This is still one big figure. each subplot is just different axis(x,y) located on different locations in this big Figure. 

Here is a few action

There are million ways people can use one software, however, this is my preferred way (may not be optimal, but workable). 
As a data scientist, mostly I want to use Plotly for interactive exploratory analytics since it provides way to get better feeling about data.