How to plot stacked 100% bar plot with seaborn for categorical data-CodePudding

I have a dataset that looks like this (assume this has 4 categories in Clicked, the head(10) only showed 2 categories):

    Rank Clicked
0   2.0 Cat4
1   2.0 Cat4
2   2.0 Cat4
3   1.0 Cat1
4   1.0 Cat4
5   2.0 Cat4
6   2.0 Cat4
7   3.0 Cat4
8   5.0 Cat4
9   5.0 Cat4

This is a code that returns this plot:

eee = (df.groupby(['Rank','Clicked'])['Clicked'].count()/df.groupby(['Rank'])['Clicked'].count())
eee.unstack().plot.bar(stacked=True)
plt.legend(['Cat1','Cat2','Cat3','Cat4'])
plt.xlabel('Rank')

Is there a way to achieve this with seaborn (or matplotlib) instead of the pandas plotting capabilities? I tried a few ways, both of running the seaborn code and of preprocessing the dataset so it's on the correct format, with no luck.

CodePudding user response：

e.g.

tips = sns.load_dataset("tips")
sns.histplot(
    data=tips,
    x="size", hue="day",
    multiple="fill", stat="proportion",
    discrete=True, shrink=.8
)

CodePudding user response：

Seaborn doesn't support stacked barplot, so you need to plot the cumsum:

# calculate the distribution of `Clicked` per `Rank`
distribution = pd.crosstab(df.Rank, df.Clicked, normalize='index')

# plot the cumsum, with reverse hue order
sns.barplot(data=distribution.cumsum(axis=1).stack().reset_index(name='Dist'),
            x='Rank', y='Dist', hue='Clicked',
            hue_order = distribution.columns[::-1],   # reverse hue order so that the taller bars got plotted first
            dodge=False)

Output:

Preferably, you can also reverse the cumsum direction, then you don't need to reverse hue order:

sns.barplot(data=distribution.iloc[:,::-1].cumsum(axis=1)       # we reverse cumsum direction here
                       .stack().reset_index(name='Dist'),
            x='Rank', y='Dist', hue='Clicked',
            hue_order=distribution.columns,                     # forward order
            dodge=False)

Output: