Advanced visualization tutorial¶
Next to the high level plotting function mendeleev.vis.periodic_table
, mendeleev
offers two lower level functions that give you more control over the result. There are two plotting backends supported:
Note¶
Depending on your environment being the classic jupyter notebook or jupyterlab you might have to do additional configuration steps, so if you’re not getting expected results see plotly of bokeh documentation.
Accessing lower level plotting functions¶
There are two plotting functions, one for each of the backends:
periodic_table_plotly
inmendeleev.vis.plotly
periodic_table_bokeh
inmendeleev.vis.bokeh
that you can use to customize the visualizations even further.
Both functions take the same keyword arguments as the periodic_table
function but the also require a DataFrame
with periodic table data. That dataframe needs to have x
and y
columns for each element that play the role of coordinates. You can get the default data using the create_vis_dataframe
function. Let’s start with an example using the plotly
backend.
[1]:
from mendeleev.vis import create_vis_dataframe, periodic_table_plotly
The function has only one required argument which is the data itself.
[2]:
elements = create_vis_dataframe()
periodic_table_plotly(elements)