Seaborn stacked histogram/barplot

user_10638608 0
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I have a pandas.DataFrame and I want to plot a graph based on two columns: Age (int), Survived (int - 0 or 1). Now I have something like this:

enter image description here

This is the code I use:

class DataAnalyzer:

    def _facet_grid(self, func, x: List[str], col: str = None, row: str = None) -> None:
        g = sns.FacetGrid(self.train_data, col=col, row=row)
        if func == sns.barplot:
            g.map(func, *x, ci=None)
        else:
            g.map(func, *x)
        g.add_legend()
        plt.show()

    def analyze(self) -> None:
        # Check if survival rate is connected with Age
        self._facet_grid(plt.hist, col='Survived', x=['Age'])

So this is shown on two subplots. This is good, but its harder to see the difference between the amount of records which have 0 vs 1 in the Survived column, for the particular age range.

So I want to have something like this:

enter image description here

In this scenario you could see this difference. Is there some way to do it on seaborn (cuz there I can easily operate on pandas.DataFrame)? I don't want to use vanilla matplotlib if that's possible

pandas seaborn matplotlib python
2 Answers

Starting seaborn 0.11.0, you can do this

# stacked histogram
import matplotlib.pyplot as plt
f = plt.figure(figsize=(7,5))
ax = f.add_subplot(1,1,1)

# mock your data frame
import pandas as pd
import numpy as np
_df = pd.DataFrame({
    "age":np.random.normal(30,30,1000),
    "survived":np.random.randint(0,2,1000)
})

# plot
import seaborn as sns
sns.histplot(data=_df, ax=ax, stat="count", multiple="stack",
             x="age", kde=False,
             palette="pastel", hue="survived",
             element="bars", legend=True)
ax.set_title("Seaborn Stacked Histogram")
ax.set_xlabel("Age")
ax.set_ylabel("Count")

enter image description here

user_23636270

Just stack the total histogram with the survived -0 one. It's hard to give the exact function without the precise form of the dataframe, but here's a basic example with one of seaborn examples dataset.

import matplotlib.pyplot as plt 
import seaborn as sns 
tips = sns.load_dataset("tips") 
sns.distplot(tips.total_bill, color="gold", kde=False, hist_kws={"alpha": 1}) 
sns.distplot(tips[tips.sex == "Female"].total_bill, color="blue", kde=False, hist_kws={"alpha":1}) 
plt.show()
user_108236390
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