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How to plot the relation between 3 categroical features in python

I am working on the German Credit Risk Analysis data, the target label is the type of risk whether it's good or bad risk, I am interested in plotting the relation between three categroical variables which are: Risk type, Credit history, and Purpose. I saw this Question, and I followed the answer by writing this code:

from matplotlib.cm import get_cmap

grouped = data.groupby(by=['Credit history','Purpose','Label']).count().unstack()

# List of blood types, to use later as categories in subplots
kinds = grouped.columns.levels[1]

# colors for bar graph
colors = [get_cmap('viridis')(v) for v in np.linspace(0,1,len(kinds))]

sns.set(context="talk")
nxplots = len(grouped.index.levels[0])
nyplots = len(grouped.index.levels[1])
fig, axes = plt.subplots(nxplots,
                         nyplots,
                         sharey=True,
                         sharex=True,
                         figsize=(10,12))

fig.suptitle('Credit history, purpose, and RiskType')

# plot the data
for a, b in enumerate(grouped.index.levels[0]):
    for i, j in enumerate(grouped.index.levels[1]):
        axes[a,i].bar(kinds,grouped.loc[b,j],color=colors)
        axes[a,i].xaxis.set_ticks([])

axeslabels = fig.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.grid(False)
axeslabels.set_ylabel('City',rotation='horizontal',y=1,weight="bold")
axeslabels.set_xlabel('Purpose',weight="bold")

# x- and y-axis labels
for i, j in enumerate(grouped.index.levels[1]):
    axes[nyplots,i].set_xlabel(j)
for i, j in enumerate(grouped.index.levels[0]):
    axes[i,0].set_ylabel(j)

# Tune this manually to make room for the legend
fig.subplots_adjust(right=0.82)

fig.legend([Patch(facecolor = i) for i in colors],
           kinds,
           title="Blood type",
           loc="center right")

However, I got the following error:

ValueError: shape mismatch: objects cannot be broadcast to a single shape

I don't know why, also is there any better way to visualize the relation between three categorical features?


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