Creating a matplotlib or seaborn histogram which uses percent rather than count?

Question

Specifically I'm dealing with the Kaggle Titanic dataset. I've plotted a stacked histogram which shows ages that survived and died upon the titanic. Code below.

figure = plt.figure(figsize=(15,8))
plt.hist([data[data['Survived']==1]['Age'], data[data['Survived']==0]['Age']], stacked=True, bins=30, label=['Survived','Dead'])
plt.xlabel('Age')
plt.ylabel('Number of passengers')
plt.legend()

I would like to alter the chart to show a single chart per bin of the percentage in that age group that survived. E.g. if a bin contained the ages between 10-20 years of age and 60% of people aboard the titanic in that age group survived, then the height would line up 60% along the y-axis.

Edit: I may have given a poor explanation to what I'm looking for. Rather than alter the y-axis values, I'm looking to change the actual shape of the bars based on the percentage that survived.

The first bin on the graph shows roughly 65% survived in that age group. I would like this bin to line up against the y-axis at 65%. The following bins look to be 90%, 50%, 10% respectively, and so on.

The graph would end up actually looking something like this:

Answer 1

Perhaps the following will help ...

1. Split the dataframe based on 'Survived'

   df_survived=df[df['Survived']==1]
   df_not_survive=df[df['Survived']==0]
   

2. Create Bins

   age_bins=np.linspace(0,80,21)
   

3. Use np.histogram to generate histogram data

   survived_hist=np.histogram(df_survived['Age'],bins=age_bins,range=(0,80))
   not_survive_hist=np.histogram(df_not_survive['Age'],bins=age_bins,range=(0,80))
   

4. Calculate survival rate in each bin

   surv_rates=survived_hist[0]/(survived_hist[0]+not_survive_hist[0])
   

5. Plot

   plt.bar(age_bins[:-1],surv_rates,width=age_bins[1]-age_bins[0])
   plt.xlabel('Age')
   plt.ylabel('Survival Rate')
   

Answer 2

For Seaborn, use the parameter stat. According to the documentation, currently supported values for the stat parameter are:

• count shows the number of observations
• frequency shows the number of observations divided by the bin width
• density normalizes counts so that the area of the histogram is 1
• probability normalizes counts so that the sum of the bar heights is 1

The result with stat being count:

seaborn.histplot(
    data=data,
    x='variable',
    discrete=True,
    stat='count'
)

The result after stat is changed to probability:

seaborn.histplot(
    data=data,
    x='variable',
    discrete=True,
    stat='probability'
)