Plot chart like heat map with values from one column and color from another column

I have the following dataset with many rows, multiple samples and 3 columns. I need to plot a graph which looks like a heatmap but it should fill the color not just for the position but also till previous position.

Name	position	category
Sample1	15500	1
Sample1	15800	2
Sample1	16200	2
Sample1	17200	3
Sample1	17400	3
Sample1	17700	3
Sample1	18300	2
Sample1	20010	2
Sample1	22120	1
Sample1	30000	3
Sample2	15880	1
Sample2	16200	1
Sample2	16900	3
Sample2	18200	3
Sample2	18500	2
Sample2	20400	1
Sample2	21300	2
Sample2	24800	3
Sample2	26000	1
Sample2	30000	3

I first converted this to a pivot table

import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
    
sample_pivot=sample.pivot_table(columns="position" , index= "Name", values="category")

Then i used plotly to chart this

fig = px.imshow(sample_pivot)
fig.update_xaxes(range=[1, 35000])
fig.show() 

I got the following chart

I need to modify above chart to fill the color in between the positions like below where the color filling will trace back to previous position and show up on the chart

Sample 1 : 
1-15500 --> Dark blue
15501-16200 --> orange red
16201-17700 --> yellow
17701-20010 --> orange red
20011-22120 --> Dark blue
22121-30000 --> yellow

One way i was thinking to generate what i wanted to get above is to have numbers in increments of 100 like 100,200,300,400 ... till 30,000 and fill blanks with Nan, perform outer join so that i have all positions including the incremented positions and use .bfill() method to fill and create above graph. Any suggestions on this or any better approach ?

You can do the following :

• In order to fill the region from 1 to the next known position, insert the corresponding column
  sample_pivot.insert(0, 1, np.nan)

• Fill missing/NaN values using pandas.DataFrame.bfill
  sample_pivot.bfill(axis=1, inplace=True)

Note also that since the underlying trace used in this case is a plotly.graph_objects.Heatmap, you could use the parameter connectgaps, although it won't do exactly what you want, it might be interesting for you to see what it does.