How to make colorscale consistant for all subplots

📊 Plotly Python
1

I encountered this post Trying to make a uniform colorscale for each of the subplots and wonder if one can do the same as the solution there if one is using px (instead of go)?

Consider the code below:

data = {
    'start': ['2023-01-01', '2023-02-01', '2023-03-01'],
    'category': ['month', 'year', 'year'],
    'duration': [5, 10, 15,]
}
data2 = {
    'start': ['2023-02-01', '2023-03-01', '2023-04-01'],
    'category': ['year', 'year', 'month'],
    'duration': [5, 10, 15]
}
df = pd.DataFrame(data)
df2 = pd.DataFrame(data2)

fig = make_subplots(rows=2, cols=1, shared_xaxes=True)
fig1 = px.bar(df, x='start', y="duration", color='category', orientation='h', color_discrete_sequence=px.colors.qualitative.Vivid)
for trace in fig1.data:
    fig.add_trace(trace, 1, 1)

fig2 = px.bar(df2, x='start', y="duration", color='category', orientation='h', color_discrete_sequence=px.colors.qualitative.Vivid)
for trace in fig2.data:
    fig.add_trace(trace, 2, 1)
    
fig.show()

How can I make sure that year bars are the same color in both subplots? Or, do I have to write a manual mapping? If one must do manual mapping, is there a way to map all existing categorical values (e.g., month and year etc) to an existing color sequence like Vivid? But perhaps there’s an easier way?

image
image950×573 4.56 KB

2

Hi @Peilin you can update the traces and assign a given color to your category:

fig.update_traces({'marker':{'color':'gray'}}, selector={'name': 'year'})
fig.update_traces({'marker':{'color':'gold'}}, selector={'name': 'month'})

If you want to hide the legend entries (which I assume you want to do) you can do it like this:

fig.update_traces(showlegend=False, selector=0)
fig.update_traces(showlegend=False, selector=1)

newplot
newplot857×360 9.09 KB

1 Like
3

Great, thanks for the pointer and inspiration! I have then looped through the color scale and assigned the category values in order to do the update_traces in a loop. Thanks a bunch!

1 Like