Plot enum values in dataframe using pyplot segmented bar - pandas

Question

I have a Series of (timestamp, enum value) describing when a system was in a given state; the state is described by the enum.
Time | State
--------------
0 | A
3 | B
4 | A
7 | C
9 | D
I'd like to visualize the state changes in a bar plot by filling each state forward to the next timestamp, and using a different color for each enum value:
|
|__________________________________________
| A | B | A | C | D |
|___________|___|___________|_______|_____|
|
---------------------------------------------
0 1 2 3 4 5 6 7 8 9 10
Any advice? I've looked into Line Collections and horizontal bars, but Line Collections seem clunky and hbar seems to be for scalar values. I'm hoping to find an elegant idiomatic solution.

Answer 1

You can create bar charts specifying left starting points and widths:
color = {'A': 'red', 'B': 'green', 'C': 'blue', 'D': 'yellow'}
for s, t, c in list(zip(df.State, df.Time.shift(-1) - df.Time, df.Time))[: -1]:
bar(left=c, height=0.8, width=t, bottom=0, color=color[s], orientation="horizontal", label=s)
print(c, t)
legend();
You can also call
get_yaxis().set_visible(False)
use better colors, and make this figure less ugly (it's hard to make it more ugly).

Answer 2

I came up with a solution that uses LineCollection. My dissatisfaction with it is that the LineCollection elements seem scale-invariant (they appear the same width no matter what the y-axis scale is), which makes it hard to manipulate. Because of this shortcoming I think I prefer the bar solution.
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.patches as mpatches
import pandas as pd
df = pd.DataFrame(zip([0, 3, 4, 7, 9, 12], ['A', 'B', 'A', 'C', 'D', 'A']),
columns=['Time', 'State'])
df['Duration'] = df['Time'].shift(-1) - df['Time']
# Showing how we can use HTML color names
state_to_color_map = {
'A': 'LightBlue',
'B': 'gray',
'C': 'LightPink',
'D': 'MediumSeaGreen'
}
fig = plt.figure(figsize=(8, 4))
ax = fig.gca()
plot_height = 0 # Can loop this to plot multiple bars
for state, state_color in state_to_color_map.iteritems():
segments = [[(start, plot_height), (start + duration, plot_height)] for
(start, duration) in
df[df['State'] == state][['Time', 'Duration']].values]
plot_segments = mpl.collections.LineCollection(
segments=segments,
# In matplotlib 2.2.2, this code is `mcolors.to_rgba(...)`
# Use this code for matplotlib 1.5.3.
colors=[mcolors.colorConverter.to_rgba(state_color)] * len(segments),
linewidths=50)
ax.add_collection(plot_segments)
ax.set_ylim(-1, 1)
ax.set_xlim(0, 12)
# Legend
patches = []
for state, color in sorted(state_to_color_map.iteritems()):
patches.append(mpatches.Patch(color=color, label=state))
ax.legend(handles=patches, bbox_to_anchor=(1.10, 0.5), loc='center',
borderaxespad=0.)