Change the order of bars in a grouped barplot with hvplot/holoviews

Question

I try to create a grouped bar plot but can't figure out how to influence the order of the barplot.

Given these example data:

import pandas as pd
import hvplot.pandas

df = pd.DataFrame({
    "lu": [200, 100, 10], 
    "le": [220, 80, 130], 
    "la": [60, 20, 15], 
    "group": [1, 2, 2],
})
df = df.groupby("group").sum()

I'd like to create a horizontal grouped bar plot showing the two groups 1 and 2 with all three columns. The columns should appear in the order of "le", "la" and "lu".

Naturally I'd try this with Hvplot:

df.hvplot.barh(x = "group", y = ["le", "la", "lu"])

With that I get the result below:

Hvplot does not seem to care about the order I add the columns (calling df.hvplot.barh(x = "group", y = ["lu", "le", "la"]) doesn't change anything. Nor does Hvplot seem to care about the original order in the dataframe.

Are there any options to influence the order of the bars?

Answer 1

This has just been fixed in HoloViews 1.13.

You can sort your barplot just like you wanted:

df.hvplot.barh(x="group", y=["lu", "la", "le"])

As I write this, HoloViews 1.13 is not officially available yet, but you can install it through:

pip install git+https://github.com/holoviz/holoviews.git

If you want even more control over the order, you can use .redim.values() on your grouped_barplot:

group_specific_order = [2, 1]
variable_specific_order = ['lu', 'la', 'le']

# Note that group and Variable are the variable names of your dimensions here
# when you use this on a different grouped barchart, then please change to the 
# names of your own dimensions.
your_grouped_barplot.redim.values(
    group=group_specific_order, 
    Variable=variable_specific_order,
)