Stacked bar chart for a pandas df

Question

I have a df like this and would like to plot stacked bar chart where in the x axis is Component and the y-axis shows the count by 'Major', 'Minor' etc.

       Component Priority
0  Browse Groups    Minor
1  Notifications    Major
2             BI    Major
3             BI    Minor
4             BI    Minor

For example, the first bar would have 1st component with a count of 1 minor,..so on.. and 3rd would have 'BI' in x-axis with 1 count of Major and 2 counts of Minor stacked. What is the simplest way to do this in seaborn or something similar?

Answer 1

You can groupby both columns and count on Priority, then unstack and plot as stacked bar chart:

df.groupby(['Component', 'Priority']).Priority.count().unstack().plot.bar(stacked=True)

Example:

import pandas as pd

df = pd.DataFrame({'Component': list('abccc'), 'Priority': ['Minor', 'Major', 'Major', 'Minor', 'Minor']})
df.groupby(['Component', 'Priority']).Priority.count().unstack().plot.bar(stacked=True)

As an alternative, you can use a crosstab:

pd.crosstab(df.Component, df.Priority).plot.bar(stacked=True)

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If you want to use seaborn (I only now saw the seaborn tag), you can use a displot:

import seaborn as sns

sns.displot(x='Component', hue='Priority', data=df, multiple='stack')