How to plot sequential data, changing the color according to cluster

Question

I have a dataframe with information concerning the date and the cluster that it belongs (it was done before based on collected temperatures for each day). I want to plot this data in sequence, like a stacked bar chart, changing the color of each element according to the assigned cluster. Here it is my table (the info goes up to 100 days):

Date	order	ClusterNo2	constant
2020-08-07	1	3.0	1
2020-08-08	2	0.0	1
2020-08-09	3	1.0	1
2020-08-10	4	3.0	1
2020-08-11	5	1.0	1
2020-08-12	6	1.0	1
2020-08-13	7	3.0	1
2020-08-14	8	2.0	1
2020-08-15	9	2.0	1
2020-08-16	10	2.0	1
2020-08-17	11	2.0	1
2020-08-18	12	1.0	1
2020-08-19	13	1.0	1
2020-08-20	14	0.0	1
2020-08-21	15	0.0	1
2020-08-22	16	1.0	1

Obs: I can't simply group the data by cluster because the plot should be sequential. I thought writing a code to identify the number of elements of each cluster sequentially, but then I will face the same problem for plotting. Someone know how to solve this?

The expected result should be something like this (the numbers inside the bar representing the cluster, the x-axis the time in days and the bar width the number of observed days with the same cluster in order :

Answer 1

You could just plot a normal bar graph, with 1 bar corresponding to 1 day. If you make the width also 1, it will look as if the patches are contiguous.

import numpy as np
import matplotlib.pyplot as plt

from matplotlib.colors import BoundaryNorm

# simulate data
total_datapoints = 16
total_clusters = 4
order = np.arange(total_datapoints)
clusters = np.random.randint(0, total_clusters, size=total_datapoints)

# map clusters to colors
cmap = plt.cm.tab10
bounds = np.arange(total_clusters + 1)
norm = BoundaryNorm(bounds, cmap.N)
colors = [cmap(norm(cluster)) for cluster in clusters]

# plot
fig, ax = plt.subplots()
ax.bar(order, np.ones_like(order), width=1, color=colors, align='edge')

# xticks
change_points = np.where(np.diff(clusters) != 0)[0] + 1
change_points = np.unique([0] + change_points.tolist() + [total_datapoints])
ax.set_xticks(change_points)

# annotate clusters
for ii, dx in enumerate(np.diff(change_points)):
    xx = change_points[ii] + dx/2
    ax.text(xx, 0.5, str(clusters[int(xx)]), ha='center', va='center')

ax.set_xlabel('Time (days)')
plt.show()