There may be limitations on using the hatch feature with certain types of plots, which can pose a problem.
However, it is possible to achieve the desired plot by utilizing two separate instances of the bar() function.
import matplotlib.pyplot as plt
import pandas as pd
data = {
"calories": [420, 380, 390],
"duration": [50, 40, 45]
}
df = pd.DataFrame(data)
fig, ax = plt.subplots()
ax.bar(["1","2","3"], df['calories'], hatch='/', color="#E69F00")
ax.bar(["1" ,"2","3"], df['duration'], hatch='*', color="#56B4E9", bottom=df['calories'])
plt.show()
https://i.stack.imgur.com/KPH3m.png
For further information, refer to the Matplotlib documentation on Stacked bar chart, Hatch demo, and Hatch style reference.
EDIT:
A different approach involves utilizing two bar() functions or setting the hatch attribute after drawing the bars.
Refer to this Stack Overflow thread for more insights.
In another version, the use of itertools.cycle proves advantageous due to the nested loops required when dealing with multiple Rectangles in a BarContainer.
import pandas as pd
import matplotlib.pyplot as plt
from cycler import cycler
bar_cycler = cycler(color=["#E69F00", "#56B4E9"])
plt.rc("axes", prop_cycle=bar_cycler)
data = {
"calories": [420, 380, 390],
"duration": [50, 40, 45]
}
df = pd.DataFrame(data)
ax = df.plot(stacked=True, kind='bar')
# ---
import itertools
cycle_hatch = itertools.cycle(["/" , "o"])
for container, hatch in zip(ax.containers, cycle_hatch):
for item in container.patches:
item.set_hatch(hatch)
# ---
plt.show()
EDIT:
Another similar query involved setting different hatching styles for each Rectangle within a BarContainer.
More details are available in this Stack Overflow post.